Showing papers on "Rough set published in 2008"

Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

Abstract: Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selection described through the use of real-world applications and worked examples Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web

Attributes Reduction Using Fuzzy Rough Sets

Abstract: Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets are mainly concentrated on the construction of approximation operators. Less effort has been put on the attributes reduction of databases with fuzzy rough sets. This paper mainly focuses on the attributes reduction with fuzzy rough sets. After analyzing the previous works on attributes reduction with fuzzy rough sets, we introduce formal concepts of attributes reduction with fuzzy rough sets and completely study the structure of attributes reduction. An algorithm using discernibility matrix to compute all the attributes reductions is developed. Based on these lines of thought, we set up a solid mathematical foundation for attributes reduction with fuzzy rough sets. The experimental results show that the idea in this paper is feasible and valid.

Computational Intelligence: Methods and Techniques

Abstract: This book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. Those techniques are today commonly applied issues of artificial intelligence, e.g. to process speech and natural language, build expert systems and robots. The first part of the book presents methods of knowledge representation using different techniques, namely the rough sets, type-1 fuzzy sets and type-2 fuzzy sets. Next various neural network architectures are presented and their learning algorithms are derived. Moreover, the family of evolutionary algorithms is discussed, in particular the classical genetic algorithm, evolutionary strategies and genetic programming, including connections between these techniques and neural networks and fuzzy systems. In the last part of the book, various methods of data partitioning and algorithms of automatic data clustering are given and new neuro-fuzzy architectures are studied and compared.

Mixed feature selection based on granulation and approximation

Abstract: Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mainly designed for dealing with numerical or categorical attributes. However, data usually comes with a mixed format in real-world applications. In this paper, we generalize Pawlak's rough set model into @d neighborhood rough set model and k-nearest-neighbor rough set model, where the objects with numerical attributes are granulated with @d neighborhood relations or k-nearest-neighbor relations, while objects with categorical features are granulated with equivalence relations. Then the induced information granules are used to approximate the decision with lower and upper approximations. We compute the lower approximations of decision to measure the significance of attributes. Based on the proposed models, we give the definition of significance of mixed features and construct a greedy attribute reduction algorithm. We compare the proposed algorithm with others in terms of the number of selected features and classification performance. Experiments show the proposed technique is effective.

A rough set enhanced fuzzy approach to quality function deployment

Abstract: Quality function deployment (QFD) provides a systematic methodology to assist companies in developing quality products that are able to satisfy customer needs. The house of quality (HOQ), as the first phase of QFD, plays the most important role in product development. Frequently, fuzzy numbers are used to quantify the vagueness of linguistic terms so as to facilitate subjective assessments in the HOQ. However, the issue concerning how to determine the boundary intervals of fuzzy numbers remains unresolved. This work proposes a novel approach based on rough set theory, and introduces two concepts called rough number and rough boundary interval to address this issue. A comparative case study presented in this work shows that the proposed approach has significant advantages compared to the prevailing fuzzy number based method in processing subjective linguistic assessments in QFD.

Combination entropy and combination granulation in rough set theory

Abstract: Based on the intuitionistic knowledge content nature of information gain, the concepts of combination entropy and combination granulation are introduced in rough set theory. The conditional combination entropy and the mutual information are defined and their several useful properties are derived. Furthermore, the relationship between the combination entropy and the combination granulation is established, which can be expressed as CE(R) + CG(R) = 1. All properties of the above concepts are all special instances of those of the concepts in incomplete information systems. These results have a wide variety of applications, such as measuring knowledge content, measuring the significance of an attribute, constructing decision trees and building a heuristic function in a heuristic reduct algorithm in rough set theory.

Approximations and reducts with covering generalized rough sets

Abstract: The covering generalized rough sets are an improvement of traditional rough set model to deal with more complex practical problems which the traditional one cannot handle. It is well known that any generalization of traditional rough set theory should first have practical applied background and two important theoretical issues must be addressed. The first one is to present reasonable definitions of set approximations, and the second one is to develop reasonable algorithms for attributes reduct. The existing covering generalized rough sets, however, mainly pay attention to constructing approximation operators. The ideas of constructing lower approximations are similar but the ideas of constructing upper approximations are different and they all seem to be unreasonable. Furthermore, less effort has been put on the discussion of the applied background and the attributes reduct of covering generalized rough sets. In this paper we concentrate our discussion on the above two issues. We first discuss the applied background of covering generalized rough sets by proposing three kinds of datasets which the traditional rough sets cannot handle and improve the definition of upper approximation for covering generalized rough sets to make it more reasonable than the existing ones. Then we study the attributes reduct with covering generalized rough sets and present an algorithm by using discernibility matrix to compute all the attributes reducts with covering generalized rough sets. With these discussions we can set up a basic foundation of the covering generalized rough set theory and broaden its applications.

Rough – Granular Computing in Knowledge Discovery and Data Mining

Abstract: The book "Rough-Granular Computing in Knowledge Discovery and Data Mining" written by Professor Jaroslaw Stepaniuk is dedicated to methods based on a combination of the following three closely related and rapidly growing areas: granular computing, rough sets, and knowledge discovery and data mining (KDD). In the book, the KDD foundations based on the rough set approach and granular computing are discussed together with illustrative applications. In searching for relevant patterns or in inducing (constructing) classifiers in KDD, different kinds of granules are modeled. In this modeling process, granules called approximation spaces play a special rule. Approximation spaces are defined by neighborhoods of objects and measures between sets of objects. In the book, the author underlines the importance of approximation spaces in searching for relevant patterns and other granules on different levels of modeling for compound concept approximations. Calculi on such granules are used for modeling computations on granules in searching for target (sub) optimal granules and their interactions on different levels of hierarchical modeling. The methods based on the combination of granular computing, the rough and fuzzy set approaches allow for an efficient construction of the high quality approximation of compound concepts.

Transactions on Rough Sets III

Abstract: Regular Papers.- Flow Graphs and Data Mining.- The Rough Set Exploration System.- Rough Validity, Confidence, and Coverage of Rules in Approximation Spaces.- Knowledge Extraction from Intelligent Electronic Devices.- Processing of Musical Data Employing Rough Sets and Artificial Neural Networks.- Computational Intelligence in Bioinformatics.- Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces.- Approximation Spaces and Information Granulation.- The Rough Set Database System: An Overview.- Rough Sets and Bayes Factor.- Formal Concept Analysis and Rough Set Theory from the Perspective of Finite Topological Approximations.- Dissertations and Monographs.- Time Complexity of Decision Trees.

A grey-based rough decision-making approach to supplier selection

Abstract: In this paper, we propose a grey-based rough set approach to deal with the supplier selection in supply chain management. The proposed approach takes advantage of mathematical analysis power of grey system theory while at the same time utilizing data mining and knowledge discovery power of rough set theory. It is suitable to the decision-making under more uncertain environments. We also provide a viewpoint on the attribute values in rough set decision table under the condition that all alternatives are described by linguistic variables that can be expressed in grey number. The most suitable supplier can be determined by grey relational analysis based on grey number. A case of supplier selection was used to validate the proposed approach.

Hierarchical Classifiers for Complex Spatio-temporal Concepts

Abstract: The aim of the paper is to present rough set methods of constructing hierarchical classifiers for approximation of complex concepts. Classifiers are constructed on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. Information systems, decision tables and decision rules are basic tools for modeling and constructing such classifiers. The general methodology presented here is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for (un)structured complex objects, to identify the behavioral patterns of complex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio-temporal concepts requiring approximation. We describe the results of computer experiments performed on real-life data sets from a vehicular traffic simulator and on medical data concerning the infant respiratory failure.