Effects of Diversity Measures on the Design of Ensemble Classifiers by Multiobjective Genetic Fuzzy Rule Selection with a Multi-classifier Coding Scheme

Authors:
Yusuke Nojima;Hisao Ishibuchi
Affiliations:
Dept. of Computer Science and Intelligent Systems, Graduate School of Engeering, Osaka Prefecture University, Osaka, Japan 599-8531;Dept. of Computer Science and Intelligent Systems, Graduate School of Engeering, Osaka Prefecture University, Osaka, Japan 599-8531
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

Citing 12
Cited 0

Bagging predictors

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

Fuzzy Sets and Systems
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Three objective genetics-based machine learning for linguisitc rule extraction

Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems

Fuzzy Optimization and Decision Making
Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)

Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing)
An analysis of diversity measures

Machine Learning
Designing Fuzzy Ensemble Classifiers by Evolutionary Multiobjective Optimization with an Entropy-Based Diversity Criterion

HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design

International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Selecting fuzzy if-then rules for classification problems using genetic algorithms

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We have already proposed multiobjective genetic fuzzy rule selection with a multi-classifier coding scheme for the design of ensemble classifiers. An entropy-based diversity measure was used as an objective to be maximized for increasing the diversity of base classifiers in an ensemble. In this paper, we examine the use of other diversity measures in the design of ensemble classifiers. Experimental results show that the choice of a diversity measure has a large effect on the performance of designed ensemble classifiers.