Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems

Ishibuchi, Hisao; Murata, Tadahiko; Türkşen, I.B.

doi:10.1016/s0165-0114(96)00098-x

Cited by 345 publications

(178 citation statements)

References 16 publications

Supporting

Mentioning

164

Contrasting

Unclassified

Order By: Relevance

“…The iris data is a common benchmark in classification and pattern recognition studies [10]. It contains 50 measurements of four features from each of the three species Iris setosa, Iris versicolor, and Iris virginica [11].…”

Section: Iris Data Classificationmentioning

confidence: 99%

Hierarchical Radial Basis Function Neural Networks for Classification Problems

Chen

Peng

Abraham

2006

Advances in Neural Networks - ISNN 2006

View full text Add to dashboard Cite

Abstract. Hierarchical neural networks consist of multiple neural networks assembled in the form of an acyclic graph. The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network can be created and evolved by using treestructure based evolutionary algorithm. This framework allows input variables selection, over-layer connections for the various nodes involved. The HRBF structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results on benchmark classification problems indicate that the proposed method is efficient.

show abstract

Section: Iris Data Classificationmentioning

confidence: 99%

Hierarchical Radial Basis Function Neural Networks for Classification Problems

Chen

Peng

Abraham

2006

Advances in Neural Networks - ISNN 2006

View full text Add to dashboard Cite

show abstract

“…We may find several methods to do so with different search algorithms in the specialized literature [15,16,23,29,32,33,38]. In [39], an interesting heuristic rule selection procedure is proposed where, by means of statistical measures, a relevance factor is computed for each fuzzy rule composing the linguistic FRBSs to subsequently select the most relevant ones.…”

Section: Selecting/merging Linguistic Rulesmentioning

confidence: 99%

Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview

Casillas

Cordón

Herrera

et al. 2003

Interpretability Issues in Fuzzy Modeling

114

View full text Add to dashboard Cite

Abstract. System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused on the interpretability, precise FM (mainly developed by Takagi-Sugeno-Kang FRBSs) is focused on the accuracy. Since both criteria are of vital importance in system modeling, the balance between them has started to pay attention in the fuzzy community in the last few years.The chapter analyzes mechanisms to find this balance by improving the interpretability in linguistic FM: selecting input variables, reducing the fuzzy rule set, using more descriptive expressions, or performing linguistic approximation; and in precise FM: reducing the fuzzy rule set, reducing the number of fuzzy sets, or exploiting the local description of the rules.

show abstract

“…A two-objective genetic algorithm was used in [10] for finding non-dominated rule sets with respect to these two objectives. Genetic rule selection was further extended to the following three-objective optimization problem in [13]:…”

Section: Heuristic Rule Extraction and Genetic Rule Selectionmentioning

confidence: 99%

Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

Ishibuchi

Yamamoto

2003

Genetic and Evolutionary Computation — GECCO 2003

Self Cite

View full text Add to dashboard Cite

Abstract. One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. In this paper, we propose an idea of using EMO algorithms for constructing an ensemble of fuzzy rule-based classifiers with high diversity. The classification of new patterns is performed based on the vote of multiple classifiers generated by a single run of EMO algorithms. Even when the classification performance of individual classifiers is not high, their ensemble often works well. The point is to generate multiple classifiers with high diversity. We demonstrate the ability of EMO algorithms to generate various non-dominated fuzzy rule-based classifiers with high diversity by their single run. Through computational experiments on some wellknown benchmark data sets, it is shown that the vote of generated fuzzy rulebased classifiers leads to high classification performance on test patterns.

show abstract

Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems

Cited by 345 publications

References 16 publications

Hierarchical Radial Basis Function Neural Networks for Classification Problems

Hierarchical Radial Basis Function Neural Networks for Classification Problems

Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview

Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers

Contact Info

Product

Resources

About