Performance Evaluation of Evolutionary Multiobjective Approaches to the Design of Fuzzy Rule-Based Ensemble Classifiers

Authors:
Hisao Ishibuchi;Yusuke Nojima
Affiliations:
Graduate School of Engineering, Osaka Prefecture University, Japan;Graduate School of Engineering, Osaka Prefecture University, Japan
Venue:
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Year:
2005

Citing 0
Cited 2

Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers

International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
Review Article: Multi-objective nature-inspired clustering and classification techniques for image segmentation

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evolutionary multiobjective fuzzy rule selection can find a large number of non-dominated fuzzy rule-based classifiers with different tradeoffs between complexity and accuracy. Very simple fuzzy rule-based classifiers with high interpretability are usually not accurate while complicated classifiers with high accuracy are not interpretable. In this paper, fuzzy rule-based classifiers with different tradeoffs are used as an ensemble classifier. Three multiobjective formulations of fuzzy rule selection are compared with each other in terms of the generalization ability of constructed ensemble classifiers. Those ensemble classifiers are also compared with individual fuzzy rule-based classifiers obtained from the corresponding three single-objective formulations based on weighted sums of accuracy and complexity measures.