论文信息 - Approaches of Individual Classifier Generation and Classifier Set Selection for Fuzzy Classifier Ensemble

Approaches of Individual Classifier Generation and Classifier Set Selection for Fuzzy Classifier Ensemble

Classifier ensemble is now an active area of research in machine learning and pattern recognition. Fuzzy classification is an important application of fuzzy set. In this paper, we propose a fuzzy classifier with kenerl fuzzy C-means clustering (KFCMC) algorithm. Based on such fuzzy classifier, the approaches of constructing fuzzy classifier ensemble system are introduced. These approaches include individual fuzzy classifier generation, individual fuzzy classifier reliability computation, fuzzy classifier set selection, and classifiers ensemble etc. Our aim is building accurate and diverse classifiers. Experiment results show that our proposed approaches are effective.

[1] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.

[2] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.

[3] Thomas G. Dietterich,et al. Pruning Adaptive Boosting , 1997, ICML.

[4] Wu Zhongdong,et al. Kernel method-based fuzzy clustering algorithm , 2005 .

[5] Lawrence O. Hall,et al. A Comparison of Ensemble Creation Techniques , 2004, Multiple Classifier Systems.

[6] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .

[7] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.

[8] Kagan Tumer,et al. Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..

[9] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[10] Christopher J. Merz,et al. UCI Repository of Machine Learning Databases , 1996 .

[11] KohaviRon,et al. An Empirical Comparison of Voting Classification Algorithms , 1999 .

[12] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[13] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[14] Subhash C. Bagui,et al. Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.