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Z. Bandar

Bio: Z. Bandar is an academic researcher from Manchester Metropolitan University. The author has contributed to research in topics: Fuzzy classification & Fuzzy set operations. The author has an hindex of 6, co-authored 12 publications receiving 90 citations.

Papers
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Proceedings ArticleDOI
23 Jul 2007
TL;DR: Experimental results indicate that significant improvements can be made in the performance of fuzzy trees when the most appropriate T-norm is optimised for a specific domain.
Abstract: The success of fuzzy decision trees when applied to classification problems is usually attributed to the selection and tuning of fuzzy sets to represent the problem domain. The impact of fuzzy inference in combining grades of membership throughout fuzzy trees has not been considered in-depth. A number of parameterized fuzzy operators based on the T-norm model have been proposed but not exploited in practical applications. This paper presents a comparative study which examines a number of T-norm and T-conorms and their application within Fuzzy Decision Trees. The methodology uses a Genetic Algorithm to tune the weights of T-norm operators and optimize fuzzy membership functions simultaneously in fuzzy trees. The paper applies the methodology to two Fuzzy Decision Tree algorithms known as FIA and Fuzzy CHAIRS. Six different T-norm models are investigated across five real world datasets. Experimental results indicate that significant improvements can be made in the performance of fuzzy trees when the most appropriate T-norm is optimised for a specific domain.

19 citations

Proceedings ArticleDOI
02 Dec 2001
TL;DR: A novel approach of overcoming this weakness through the use of fuzzy decision forests is presented, based upon the induction of multiple fuzzy decision trees from one training sample, where each tree represents a different view of the data domain.
Abstract: The creation of multiple decision trees is a relatively new concept, which aims to improve the predictive power of a single decision tree. The approach is based on the induction of more than one C4.5-type decision tree from the same training sample where each decision tree represents a different view of the same domain resulting in a network of decision tree models. The utilization of multiple decision trees has been shown to lead to an improved performance by combining multiple perspectives of the same domain thus increasing the information content whereas, in comparison, a single decision tree can only represent one restricted view of the domain. One predominant weakness in creating a single tree is the generation of sharp decision boundaries at every node within the tree, which results in small changes in attribute values giving radically different classifications. This problem becomes more apparent with the generation of multiple trees. This paper presents a novel approach of overcoming this weakness through the use of fuzzy decision forests. The approach is based upon the induction of multiple fuzzy decision trees from one training sample, where each tree represents a different view of the data domain. A genetic algorithm (GA) is used to select a series of high performance membership functions, which are then applied to branches within all decision trees in the forest. The GA will in addition optimise a pre-selected fuzzy inference technique, which will assign a degree of strength to the conjunction and disjunction of membership grades within the tree. Considerable improvements in classification accuracy over original single C4.5 (crisp) trees were obtained using two real world data sets.

18 citations

Proceedings ArticleDOI
07 May 2000
TL;DR: Initial comparisons between crisp trees and the fuzzified trees show that the fuzzy tree is more robust and produces a more balanced classification leading to improved decision-making.
Abstract: This paper investigates the fuzzification of crisp decision trees using nonlinear membership functions to soften sharp decision boundaries. A novel nonlinear fuzzy algorithm provides the framework for the investigation of four different membership functions. Using a genetic algorithm (GA), various sized fuzzy regions are optimised from a training set and are applied to all decision nodes. A new case passing through the tree will result in a membership grade being generated at each branch. Three different fuzzy inference mechanisms, also optimised by the GA, are used to investigate the degree of interaction between membership grades on each specific decision path. Initial comparisons between crisp trees and the fuzzified trees show that the fuzzy tree is more robust and produces a more balanced classification leading to improved decision-making.

14 citations

Proceedings ArticleDOI
25 May 2005
TL;DR: Initial comparisons between crisp and fuzzified CHAID trees show that the fuzzy tree is more robust and produces a more balanced classification leading to improved decision making.
Abstract: This paper introduces a novel algorithm which applies the theories of fuzzification in order to fuzzify decision trees for solving classification problems with numeric outcomes. The CHAID algorithm is a highly efficient statistical technique for segmentation, or tree growing. The application of fuzzy logic to pre-generated CHAID decision trees can represent classification knowledge more naturally and in-line with human thinking. Using a genetic algorithm (GA), various sized fuzzy regions are optimised from a training set and are applied to all decision nodes within the tree. A new case passing through the tree results in a membership grade being generated at each branch. Four different fuzzy inference mechanisms, also optimised by the GA, are used to investigate the degree of interaction between membership grades on each specific decision path. A modified approach to Mamdani's inference is also proposed to manage the defuzzification of numeric tree outcomes. Initial comparisons between crisp and fuzzified CHAID trees show that the fuzzy tree is more robust and produces a more balanced classification leading to improved decision making

12 citations

Proceedings ArticleDOI
11 Sep 2006
TL;DR: This paper investigates a new approach to creating robust fuzzy classifiers that are impartial to the imbalance problem within data sets using raw real-world data without the need for sampling or the creation of synthetic examples.
Abstract: This paper investigates a new approach to creating robust fuzzy classifiers that are impartial to the imbalance problem within data sets. The approach uses raw real-world data without the need for sampling or the creation of synthetic examples. The aim is to achieve common currency between the actual classification accuracy and the distribution of this accuracy between the outcome classes. The proposed method first uses a fuzzy inference algorithm (FIA) to construct a fuzzy classifier from a crisp C4.5. A genetic algorithm (GA) is then used to optimize the degree of fuzziness within the classifier. The GA's fitness function consists of two components: classification accuracy and the distribution (or balance) of this accuracy between the outcomes. Both components are optimised concurrently. Four alternative fitness functions are defined, each of which applies different penalties on the classification accuracy depending on a weighting associated with the balance component. The method is then applied to three real world data sets. The results show that it is possible to attain a fuzzy classifier which exhibits both good performance and balance between outcomes regardless of any imbalance within the data set.

8 citations


Cited by
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01 Dec 2003
TL;DR: This book integrates two areas of computer science, namely data mining and evolutionary algorithms, and emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making.
Abstract: From the Publisher: This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search performed by most rule induction methods.

699 citations

Journal ArticleDOI
TL;DR: An overview of the field of GFSs, with a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process, and some potential future research directions.
Abstract: The use of genetic algorithms for designing fuzzy systems provides them with the learning and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has attracted considerable attention in the Computation Intelligence community in the last few years. This paper gives an overview of the field of GFSs, being organized in the following four parts: (a) a taxonomy proposal focused on the fuzzy system components involved in the genetic learning process; (b) a quick snapshot of the GFSs status paying attention to the pioneer GFSs contributions, showing the GFSs visibility at ISI Web of Science including the most cited papers and pointing out the milestones covered by the books and the special issues in the topic; (c) the current research lines together with a discussion on critical considerations of the recent developments; and (d) some potential future research directions.

571 citations

Journal ArticleDOI
TL;DR: The necessity of applying a preprocessing step to deal with the problem of imbalanced data-sets is analyzed and the granularity of the fuzzy partitions, the use of distinct conjunction operators, the application of some approaches to compute the rule weights and theUse of different fuzzy reasoning methods are analyzed.

279 citations

Journal ArticleDOI
01 Mar 2013
TL;DR: A new type of coherence membership function to describe fuzzy concepts, which builds upon the theoretical findings of the Axiomatic Fuzzy Set (AFS) theory, is introduced and the proposed algorithm performs significantly better than FDTs, FS-DT, KNN and C4.5.
Abstract: In this study, we introduce a new type of coherence membership function to describe fuzzy concepts, which builds upon the theoretical findings of the Axiomatic Fuzzy Set (AFS) theory. This type of membership function embraces both the factor of fuzziness (by capturing subjective imprecision) and randomness (by referring to the objective uncertainty) and treats both of them in a consistent manner. Furthermore we propose a method to construct a fuzzy rule-based classifier using coherence membership functions. Given the theoretical developments presented there, the resulting classification systems are referred to as AFS classifiers. The proposed algorithm consists of three major steps: (a) generating fuzzy decision trees by assuming some level of specificity (detailed view) quantified in terms of threshold; (b) pruning the obtained rule-base; and (c) determining the optimal threshold resulting in a final tree. Compared with other fuzzy classifiers, the AFS classifier exhibits several essential advantages being of practical relevance. In particular, the relevance of classification results is quantified by associated confidence levels. Furthermore the proposed algorithm can be applied to data sets with mixed data type attributes. We have experimented with various data commonly present in the literature and compared the results with that of SVM, KNN, C4.5, Fuzzy Decision Trees (FDTs), Fuzzy SLIQ Decision Tree (FS-DT), FARC-HD and FURIA. It has been shown that the accuracy is higher than that being obtained by other methods. The results of statistical tests supporting comparative analysis show that the proposed algorithm performs significantly better than FDTs, FS-DT, KNN and C4.5.

75 citations

Journal ArticleDOI
TL;DR: This paper addresses the discovery of knowledge in the form of prediction IF-THEN rules, which are a popular form of knowledge representation in data mining and proposes a genetic algorithm designed specifically to discover interesting fuzzy prediction rules.

46 citations