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Showing papers in "Knowledge Based Systems in 2007"


Journal ArticleDOI
TL;DR: An attribute generalization and its relation to feature selection and feature extraction are discussed and a new approach for incrementally updating approximations of a concept is presented under the characteristic relation-based rough sets.
Abstract: Any attribute set in an information system may be evolving in time when new information arrives. Approximations of a concept by rough set theory need updating for data mining or other related tasks. For incremental updating approximations of a concept, methods using the tolerance relation and similarity relation have been previously studied in literature. The characteristic relation-based rough sets approach provides more informative results than the tolerance-and-similarity relation based approach. In this paper, an attribute generalization and its relation to feature selection and feature extraction are firstly discussed. Then, a new approach for incrementally updating approximations of a concept is presented under the characteristic relation-based rough sets. Finally, the approach of direct computation of rough set approximations and the proposed approach of dynamic maintenance of rough set approximations are employed for performance comparison. An extensive experimental evaluation on a large soybean database from MLC shows that the proposed approach effectively handles a dynamic attribute generalization in data mining.

277 citations


Journal ArticleDOI
TL;DR: New results of the work in developing design principles and algorithms for constructing explanation interfaces are presented and the effectiveness of these principles are shown via a significant-scale user study in which an interface developed based on these principles is compared with a traditional one.
Abstract: A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model for recommender agents with a focus on the agent's trustworthiness as derived from the user's perception of its competence and especially its ability to explain the recommended results. We present in this article new results of our work in developing design principles and algorithms for constructing explanation interfaces. We show the effectiveness of these principles via a significant-scale user study in which we compared an interface developed based on these principles with a traditional one. The new interface, called the organization interface where results are grouped according to their tradeoff properties, is shown to be significantly more effective in building user trust than the traditional approach. Users perceive it more capable and efficient in assisting them to make decisions, and they are more likely to return to the interface. We therefore recommend designers to build trust-inspiring interfaces due to their high likelihood to increase users' intention to save cognitive effort and the intention to return to the recommender system.

220 citations


Journal ArticleDOI
TL;DR: It is concluded that a fusion of the two fields can lead to developing negotiation techniques for chatbots and the enhancement of the Open Learner Model, and this technology, if successful, could have widespread application in schools, universities and other training scenarios.
Abstract: There is an extensive body of work on Intelligent Tutoring Systems: computer environments for education, teaching and training that adapt to the needs of the individual learner. Work on personalisation and adaptivity has included research into allowing the student user to enhance the system's adaptivity by improving the accuracy of the underlying learner model. Open Learner Modelling, where the system's model of the user's knowledge is revealed to the user, has been proposed to support student reflection on their learning. Increased accuracy of the learner model can be obtained by the student and system jointly negotiating the learner model. We present the initial investigations into a system to allow people to negotiate the model of their understanding of a topic in natural language. This paper discusses the development and capabilities of both conversational agents (or chatbots) and Intelligent Tutoring Systems, in particular Open Learner Modelling. We describe a Wizard-of-Oz experiment to investigate the feasibility of using a chatbot to support negotiation, and conclude that a fusion of the two fields can lead to developing negotiation techniques for chatbots and the enhancement of the Open Learner Model. This technology, if successful, could have widespread application in schools, universities and other training scenarios.

202 citations


Journal ArticleDOI
Zeshui Xu1
TL;DR: The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables.
Abstract: The aim of this paper is to investigate the multiple attribute decision making problems with linguistic information, in which the information about attribute weights is incompletely known, and the attribute values take the form of linguistic variables. We first introduce some approaches to obtaining the weight information of attributes, and then establish an optimization model based on the ideal point of attribute values, by which the attribute weights can be determined. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the numerical weighting linguistic average (NWLA) operator to aggregate the linguistic variables corresponding to each alternative, and then rank the alternatives by means of the aggregated linguistic information. Finally, the developed method is applied to the ranking and selection of propulsion/manoeuvring system of a double-ended passenger ferry.

190 citations


Journal ArticleDOI
Mark Hall1
TL;DR: Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Baye and, in many cases, improves it dramatically.
Abstract: The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness - the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the final model. In this paper we present a simple filter method for setting attribute weights for use with naive Bayes. Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Bayes and, in many cases, improves it dramatically. The main advantages of this method compared to other approaches for improving naive Bayes is its run-time complexity and the fact that it maintains the simplicity of the final model.

174 citations


Journal ArticleDOI
TL;DR: Research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning is described and the probabilistic user model designed to capture these behaviors with the help of on- line information on user attention patterns derived from eye- tracking data is illustrated.
Abstract: In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy.

144 citations


Journal ArticleDOI
TL;DR: A real-time knowledge support framework for the development of an RFID-multi-agent based process knowledge-based system which has the ability to solve dynamic logistics process management problems.
Abstract: Purpose: This paper proposes a real-time knowledge support framework for the development of an RFID-multi-agent based process knowledge-based system which has the ability to solve dynamic logistics process management problems. Design/methodology/approach: The proposed system is developed with ''real-time process management'' capability which automatically identifies current process status, performs the process logic checking/reasoning, and, provides process knowledge support to staff members when they are tackling logistics activity problems. The unique feature of this on-line knowledge-based system, which enables it to enhance the performance of logistics organizations, is a process management engine incorporating radio-frequency identification (RFID) and multi-agent (MA) technologies. Findings: The capability of the proposed system is demonstrated through an application case study in Eastern Worldwide Company Limited. The result reveals that both performance of operations and the utilization of resources have improved significantly. Originality/value: The proposed system is a novel approach which leverages logistics performance and facilitates the creation of a learning organization through the provision of real-time knowledge support for those who handle logistics operations. Article type: Research Paper.

124 citations


Journal ArticleDOI
TL;DR: Experiments with both synthetic and real databases show that BitTableFI outperforms Apriori and CBAR which uses ClusterTable for quick support count and the algorithm can also be used in many A Priori-like algorithms to improve the performance.
Abstract: Mining frequent itemsets in transaction databases, time-series databases and many other kinds of databases is an important task and has been studied popularly in data mining research. The problem of mining frequent itemsets can be solved by constructing a candidate set of itemsets first, and then, identifying those itemsets that meet the frequent itemset requirement within this candidate set. Most of the previous research mainly focuses on pruning to reduce the candidate itemsets amounts and the times of scanning databases. However, many algorithms adopt an Apriori-like candidate itemsets generation and support count approach that is the most time-wasted process. To address this issue, the paper proposes an effective algorithm named as BitTableFI. In the algorithm, a special data structure BitTable is used horizontally and vertically to compress database for quick candidate itemsets generation and support count, respectively. The algorithm can also be used in many Apriori-like algorithms to improve the performance. Experiments with both synthetic and real databases show that BitTableFI outperforms Apriori and CBAR which uses ClusterTable for quick support count.

120 citations


Journal ArticleDOI
TL;DR: This study indicates that understanding the relationships between churns is essential in creating effective retention policy models for dealing with 'churners', and suggests that policy model construction represents an interesting and important technique in investigating the characteristics of churner groups.
Abstract: The prevention of subscriber churn through customer retention is a core issue of Customer Relationship Management (CRM). By minimizing customer churn a company maximizes its profit. This paper proposes a hybridized architecture to deal with customer retention problems. It does so not only through predicting churn probability but also by proposing retention policies. The architecture works in two modes: learning and usage. In the learning mode, the churn model learner seeks potential associations from the subscriber database. This historical information is used to form a churn model. This mode also calls for a policy model constructor to use the attributes identified in the churn model to divide all 'churners' into distinct groups. The policy model constructor is also responsible for developing a policy model for each churner group. In the usage mode, a churn predictor uses the churn model to predict the churn probability of a given subscriber. When the churn model finds that the subscriber has a high churn probability the policy model is used to suggest specific retention policies. This study's experiments show that the churn model has an evaluation accuracy of approximately eighty-five percent. This suggests that policy model construction represents an interesting and important technique in investigating the characteristics of churner groups. Furthermore, this study indicates that understanding the relationships between churns is essential in creating effective retention policy models for dealing with 'churners'.

118 citations


Journal ArticleDOI
TL;DR: The results show that data mining can be usefully applied to the problem of automatic species identification of live specimens in the field.
Abstract: A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35 different UK species, was analysed to determine if data mining techniques could be used effectively for automatic species identification. Feature vectors were extracted from each of the moth images and the machine learning toolkit WEKA was used to classify the moths by species using the feature vectors. Whereas a previous analysis of this image dataset reported in the literature [A. Watson, M. O'Neill, I. Kitching, Automated identification of live moths (Macrolepidoptera) using Digital Automated Identification System (DAISY), Systematics and Biodiversity 1 (3) (2004) 287-300.] required that each moth's least worn wing region be highlighted manually for each image, WEKA was able to achieve a greater level of accuracy (85%) using support vector machines without manual specification of a region of interest at all. This paper describes the features that were extracted from the images, and the various experiments using different classifiers and datasets that were performed. The results show that data mining can be usefully applied to the problem of automatic species identification of live specimens in the field.

108 citations


Journal ArticleDOI
TL;DR: This study addresses both fuzzy demands and multi- objective issues and proposes a fuzzy multi-objective bilevel programming model and develops an approximation branch-and-bound algorithm to solve multi- Objectives bileVEL decision problems with fuzzy demands.
Abstract: Decisions in a decentralized organization often involve two levels. The leader at the upper level attempts to optimize his/her objective but is affected by the follower; the follower at the lower level tries to find an optimized strategy according to each of possible decisions made by the leader. When model a real-world bilevel decision problem, it also may involve fuzzy demands which appear either in the parameters of objective functions or constraints of the leader or the follower or both. Furthermore, the leader and the follower may have multiple conflict objectives that should be optimized simultaneously in achieving a solution. This study addresses both fuzzy demands and multi-objective issues and propose a fuzzy multi-objective bilevel programming model. It then develops an approximation branch-and-bound algorithm to solve multi-objective bilevel decision problems with fuzzy demands. Finally, two case-based examples further illustrate the proposed model and algorithm.

Journal ArticleDOI
TL;DR: A comparative study the performance of three commonly used machine learning methods in spam filtering and tries to integrate two spam filtering methods to obtain better performance.
Abstract: The increasing volumes of unsolicited bulk e-mail (also known as spam) are bringing more annoyance for most Internet users. Using a classifier based on a specific machine-learning technique to automatically filter out spam e-mail has drawn many researchers' attention. This paper is a comparative study the performance of three commonly used machine learning methods in spam filtering. On the other hand, we try to integrate two spam filtering methods to obtain better performance. A set of systematic experiments has been conducted with these methods which are applied to different parts of an e-mail. Experiments show that using the header only can achieve satisfactory performance, and the idea of integrating disparate methods is a promising way to fight spam.

Journal ArticleDOI
TL;DR: The design and development of the AspectC++ language and weaver is described, which brings fully-fledged AOP support into the C++ domain.
Abstract: Aspect-Oriented Programming (AOP) is a programming paradigm that supports the modular implementation of crosscutting concerns. Thereby, AOP improves the maintainability, reusability, and configurability of software in general. Although already popular in the Java domain, AOP is still not commonly used in conjunction with C/C++. For a broad adoption of AOP by the software industry, it is crucial to provide solid language and tool support. However, research and tool development for C++ is known to be an extremely hard and tedious task, as the language is overwhelmed with interacting features and hard to analyze. Getting AOP into the C++ domain is not just technical challenge. It is also the question of integrating AOP concepts with the philosophy of the C++ language, which is very different from Java. This paper describes the design and development of the AspectC++ language and weaver, which brings fully-fledged AOP support into the C++ domain.

Journal ArticleDOI
TL;DR: This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents designed to give personalized suggestions based on the user's current personal preferences, other user's common preferences, and expert's domain knowledge.
Abstract: There is an increasing need for various e-service, e-commerce and e-business sites to provide personalized recommendations to on-line customers. This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents. Fuzzy cognitive agents are designed to give personalized suggestions based on the user's current personal preferences, other user's common preferences, and expert's domain knowledge. Fuzzy cognitive agents are able to represent knowledge via extended fuzzy cognitive maps, to learn users' preferences from most recent cases and to help customers make inferences and decisions through numeric computation instead of symbolic and logic deduction. A case study is included to illustrate how personalized recommendations are made by fuzzy cognitive agents in e-commerce sites. The case study demonstrates that the fuzzy cognitive agent is both flexible and effective in supporting e-commerce applications.

Journal ArticleDOI
TL;DR: This work presents a schema clustering process by organising the heterogeneous XML schemas into various groups by considering not only the linguistic and the context of the elements but also the hierarchical structural similarity.
Abstract: With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis.

Journal ArticleDOI
TL;DR: This article investigates the very fundamental concepts of strategic information systems and intelligent agent technology and integrates them into a state-of-the-art, intelligent architecture for strategic Information Systems in IT era, called intelligent agent-based SIS.
Abstract: Strategic information systems (SIS) focus on the use of information system (IS) and information technology (IT) in the strategic management process in business organizations. The emphasis is on the strategic view of IS and IT and their impact on organizational strategy. Increased competition and advances in information technologies push for considerable structural changes in SIS. Agents, as autonomous entities which either work on their own or cooperate with others, and agent architectures have enormous potentials to be applied in such critical systems. In this article, first we investigate the very fundamental concepts of strategic information systems and intelligent agent technology. Then, the discussion continues on the specification of the characteristics and implementation issues of a typical SIS. Afterwards, we make use of these concepts and integrate them into a state-of-the-art, intelligent architecture for strategic information systems, called intelligent agent-based SIS. This is a comprehensive framework for a SIS in IT era which may be put into practice by a team of professionals in the near future. The graphical representation of this model is intended to help the reader understand the concept much better. After explaining the suggested model in full details, we introduce some support agents and specify their corresponding roles in an intelligent agent-based SIS architecture. Discussions and concluding remarks regarding the proposed system are provided at the end of the paper.

Journal ArticleDOI
TL;DR: In this paper, a novel meta-search engine, named as WebFusion, is introduced, which learns the expertness of the underlying search engines in a certain category based on the users' preferences and uses the ''click-through data concept'' to give a content-oriented ranking score to each result page.
Abstract: The required information of users is distributed in the databases of various search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. Meta-search engines could provide a unified access for their users. In this paper, a novel meta-search engine, named as WebFusion, is introduced. WebFusion learns the expertness of the underlying search engines in a certain category based on the users' preferences. It also uses the ''click-through data concept'' to give a content-oriented ranking score to each result page. Click-through data concept is the implicit feedback of the users' preferences, which is also used as a reinforcement signal in the learning process, to predict the users' preferences and reduces the seeking time in the returned results list. The decision lists of underling search engines have been fused using ordered weighted averaging (OWA) approach and the application of optimistic operator as weightening function has been investigated. Moreover, the results of this approach have been compared with those achieve by some popular meta-search engines such as ProFusion and MetaCrawler. Experimental results demonstrate a significant improvement on average click rate, and the variance of clicks as well as average relevancy criterion.

Journal ArticleDOI
Z. Zenn Bien1, Hyong-Euk Lee1
TL;DR: It is shown that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth.
Abstract: HRI (Human-Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing human-friendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing the interaction process of the human-in-the loop system. We then show that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth. Two project works are briefly described to illustrate how the fuzzy logic-based learning techniques and the soft computing toolbox approach are successfully applied for human-friendly HRI systems. Next, we observe that probabilistic fuzzy rules can handle inconsistent data patterns originated from human, and show that combination of fuzzy logic, fuzzy clustering, and probabilistic reasoning in a single frame leads to an algorithm of iterative fuzzy clustering with supervision. Further, we discuss a possibility of using the algorithm for inductively constructing probabilistic fuzzy rule base in a learning system of a smart home. Finally, we propose a life-long learning system architecture for the HRI type of human-in-the-loop systems.

Journal ArticleDOI
Unil Yun1
TL;DR: This work proposes closed weighted frequent pattern mining, and presents how to discover succinct but lossless closed frequent pattern with weight constraints, which is the first work specifically to consider both constraints.
Abstract: Frequent pattern mining is one of main concerns in data mining tasks. In frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining are two main approaches to reduce the search space. Although many related studies have been suggested, no mining algorithm considers both paradigms. Even if closed frequent pattern mining represents exactly the same knowledge and weighted frequent pattern mining provides a way to discover more important patterns, the incorporation of closed frequent pattern mining and weight frequent pattern mining may loss information. Based on our analysis of joining orders, we propose closed weighted frequent pattern mining, and present how to discover succinct but lossless closed frequent pattern with weight constraints. To our knowledge, ours is the first work specifically to consider both constraints. An extensive performance study shows that our algorithm outperforms previous algorithms. In addition, it is efficient and scalable.

Journal ArticleDOI
TL;DR: This work abstracts a novel intelligent network model inspired from the NEI system, and proposes a method for Web service emergence by designing a bio-entity as an autonomous agent to represent Web service, providing a novel solution for intelligent composition and management of Web services.
Abstract: Some important mechanisms in neuroendocrine-immune (NEI) system are inspired to design a decentralized, evolutionary, scalable, and adaptive system for Web service composition and management. We first abstract a novel intelligent network model inspired from the NEI system. Based on this model, we then propose a method for Web service emergence by designing a bio-entity as an autonomous agent to represent Web service. As such, automatic composition and dynamic management of Web services can be achieved. Also, we build its computation platform which allows the bio-entities to cooperate over Web services and exploits capabilities of their partners. Finally, the simulation results on the platform show that Web service emergence can be achieved through self-organizing, cooperating, and compositing. The proposed method provides a novel solution for intelligent composition and management of Web services.

Journal ArticleDOI
TL;DR: The findings show that Investigators can be placed in one of four groups according to their ability to recover DNA and fingerprints from crime scenes, and their able to predict which crime scenes will yield the best opportunity of recovering forensic samples has no correlation to their actual ability to recovered those samples.
Abstract: This paper examines how data mining techniques can assist the monitoring of Crime Scene Investigator performance. The findings show that Investigators can be placed in one of four groups according to their ability to recover DNA and fingerprints from crime scenes. They also show that their ability to predict which crime scenes will yield the best opportunity of recovering forensic samples has no correlation to their actual ability to recover those samples.

Journal ArticleDOI
TL;DR: Three models driven by Centering Theory for discourse processing are examined: a reference model that resolves pronoun references for each question, a forward model that makes use of the forward looking centers from previous questions, and a transition model that takes into account the transition state between adjacent questions.
Abstract: Motivated by the recent effort on scenario-based context question answering (QA), this paper investigates the role of discourse processing and its implication on query expansion for a sequence of questions. Our view is that a question sequence is not random, but rather follows a coherent manner to serve some information goals. Therefore, this sequence of questions can be considered as a mini discourse with some characteristics of discourse cohesion. Understanding such a discourse will help QA systems better interpret questions and retrieve answers. Thus, we examine three models driven by Centering Theory for discourse processing: a reference model that resolves pronoun references for each question, a forward model that makes use of the forward looking centers from previous questions, and a transition model that takes into account the transition state between adjacent questions. Our empirical results indicate that more sophisticated processing based on discourse transitions and centers can significantly improve the performance of document retrieval compared to models that only resolve references. This paper provides a systematic evaluation of these models and discusses their potentials and limitations in processing coherent context questions.

Journal ArticleDOI
TL;DR: An object-oriented method for the development of a Customer Knowledge Management Information System (CKMIS) that starts from the identification of customers and their desired knowledge-accessed behaviors, through the recognition of a system architecture that supports the identification and realization of these behaviors, and finally ends with the analysis and design of the architectural classes/objects that collaborate to identify and realize these behaviors.
Abstract: For the advances of Internet technologies in recent years, electronic commerce (EC) has gained many attentions as a major theme for enterprises to keep their competitiveness. Among all possible endeavors for the EC, research has shown that effective management of customer relationships by taking advantage of customer knowledges is a major source for keeping competitive differentiation. Therefore, it is commonly recognized as an important goal for an enterprise to promote its managerial effectiveness on customer relationships through a prospective customer knowledge-based information system to achieve the so-called Customer-Oriented EC. In this paper, we present an object-oriented method for the development of such a Customer Knowledge Management Information System (CKMIS). The method starts from the identification of customers and their desired knowledge-accessed behaviors, through the recognition of a system architecture that supports the identification and realization of these behaviors, and finally ends with the analysis and design of the architectural classes/objects that collaborate to identify and realize these behaviors. The method is use case driven with UML notations utilized and extended as its modeling tool. To illustrate, the method is applied to an exemplified CKMIS for a book publishing company.

Journal ArticleDOI
TL;DR: The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects.
Abstract: This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, two concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced. The authors discuss the differences between the rough set based approaches and the fundamental entropy based method. The comparison between the presented approach and the rough set based approach and the fundamental entropy based method on some data sets from the UCI Machine Learning Repository is also reported.

Journal ArticleDOI
TL;DR: This work model the railway scheduling problem by means of domain-dependent distributed constraint models, and it is shown that these models maintained better behaviors than general distributed models based on graph partitioning.
Abstract: Many combinatorial problems can be modelled as Constraint Satisfaction Problems (CSPs). Solving a general CSP is known to be NP-complete, so closure and heuristic search are usually used. However, many problems are inherently distributed and the problem complexity can be reduced by dividing the problem into a set of subproblems. Nevertheless, general distributed techniques are not always appropriate to distribute real-life problems. In this work, we model the railway scheduling problem by means of domain-dependent distributed constraint models, and we show that these models maintained better behaviors than general distributed models based on graph partitioning. The evaluation is focused on the railway scheduling problem, where domain-dependent models carry out a problem distribution by means of trains and contiguous sets of stations.

Journal ArticleDOI
TL;DR: A new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated that will have a higher convergence speed.
Abstract: This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.

Journal ArticleDOI
TL;DR: Transformation of rough set models containing union, intersection, inverse and composition of approximation spaces is considered, finding results that will be useful to theoretical and practical researches ofrough set theory.
Abstract: This paper considers transformation of rough set models containing union, intersection, inverse and composition of approximation spaces. As special cases, approximation spaces induced from several kinds of closures of relations are also investigated. The main results given in this paper will be useful to theoretical and practical researches of rough set theory.

Journal ArticleDOI
TL;DR: A new way of discretizing numeric values using information theory that takes into account the value of the target attribute, and uses naive Bayesian classifier and C4.5 as classification tools to compare the accuracy of the method with that of other methods.
Abstract: Many classification algorithms require that training examples contain only discrete values. In order to use these algorithms when some attributes have continuous numeric values, the numeric attributes must be converted into discrete ones. This paper describes a new way of discretizing numeric values using information theory. Our method is context-sensitive in the sense that it takes into account the value of the target attribute. The amount of information each interval gives to the target attribute is measured using Hellinger divergence, and the interval boundaries are decided so that each interval contains as equal amount of information as possible. In order to compare our discretization method with some current discretization methods, several popular classification data sets are selected for discretization. We use naive Bayesian classifier and C4.5 as classification tools to compare the accuracy of our discretization method with that of other methods.

Journal ArticleDOI
TL;DR: A Knowledge Base is introduced, which consists of a database-type repository for maintaining the patterns, and rules, as an independent program that consults the pattern repository in order to improve the relation between the web site and its visitors.
Abstract: By applying web mining tools, significant patterns about the visitor behavior can be extracted from data originated in web sites. Supported by a domain expert, the patterns are validated or rejected and rules about how to use the patterns are created. This results in discovering new knowledge about the visitor behavior to the web site. But, due to frequent changes in the visitor's interests, as well as in the web site itself, the discovered knowledge may become obsolete in a short period of time. In this paper, we introduce a Knowledge Base (KB), which consists of a database-type repository for maintaining the patterns, and rules, as an independent program that consults the pattern repository. Using the proposed architecture, an artificial system or a human user can consult the KB in order to improve the relation between the web site and its visitors. The proposed structure was tested using data from a Chilean virtual bank, which proved the effectiveness of our approach.

Journal ArticleDOI
TL;DR: A fuzzy logic (FL)-based expert system (ES) has been developed to predict the results of finite element (FE) analysis, while solving a rubber cylinder compression problem.
Abstract: In this paper, a fuzzy logic (FL)-based expert system (ES) has been developed to predict the results of finite element (FE) analysis, while solving a rubber cylinder compression problem. As the performance of an ES depends on its knowledge base (KB), an attempt is made to develop the KB through three different approaches by using a genetic algorithm (GA). To collect the training data, two input parameters, namely element size and shape ratio are varied, while solving the said physical problem using an FEM package. The performance of the trained fuzzy logic-based expert system is tested for several test cases, differing significantly from the training cases. Results of these approaches are compared with those of FE analysis. Once developed, the ES is able to determine the values of parameters to be used in FE analysis, in order to obtain the results within a reasonable accuracy, at the cost of a much lower computation compared to that of the FEM package itself.