Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space

Authors:
Shinn-Ying Ho;Hung-Ming Chen;Shinn-Jang Ho;Tai-Kang Chen
Affiliations:
Dept. of Inf. Eng., Feng Chia Univ., Taichung, Taiwan;-;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2004

Citing 0
Cited 15

Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis

Information Sciences: an International Journal
Fuzzy classifier design using genetic algorithms

Pattern Recognition
Designing of classifiers based on immune principles and fuzzy rules

Information Sciences: an International Journal
A multilayered neuro-fuzzy classifier with self-organizing properties

Fuzzy Sets and Systems
A hybrid coevolutionary algorithm for designing fuzzy classifiers

Information Sciences: an International Journal
A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design

Expert Systems with Applications: An International Journal
Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification

Applied Intelligence
Learning fuzzy rules for similarity assessment in case-based reasoning

Expert Systems with Applications: An International Journal
A fuzzy rule-based classification system using interval type-2 fuzzy sets

IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Scoring method for tumor prediction from microarray data using an evolutionary fuzzy classifier

PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Intelligent particle swarm optimization in multi-objective problems

PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Evolutionary algorithm parameter tuning with sensitivity analysis

SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Breast cancer detection using cartesian genetic programming evolved artificial neural networks

Proceedings of the 14th annual conference on Genetic and evolutionary computation
Fuzzy rule-based similarity model enables learning from small case bases

Applied Soft Computing
Fast learning neural networks using Cartesian genetic programming

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

An evolutionary approach to designing accurate classifiers with a compact fuzzy-rule base using a scatter partition of feature space is proposed, in which all the elements of the fuzzy classifier design problem have been moved in parameters of a complex optimization problem. An intelligent genetic algorithm (IGA) is used to effectively solve the design problem of fuzzy classifiers with many tuning parameters. The merits of the proposed method are threefold: 1) the proposed method has high search ability to efficiently find fuzzy rule-based systems with high fitness values, 2) obtained fuzzy rules have high interpretability, and 3) obtained compact classifiers have high classification accuracy on unseen test patterns. The sensitivity of control parameters of the proposed method is empirically analyzed to show the robustness of the IGA-based method. The performance comparison and statistical analysis of experimental results using ten-fold cross validation show that the IGA-based method without heuristics is efficient in designing accurate and compact fuzzy classifiers using 11 well-known data sets with numerical attribute values.