A Dynamic Classifier Selection Method to Build Ensembles using Accuracy and Diversity

Ensemble of classifiers is an effective way of improving performance of individual classifiers. However, the choice of the ensemble members can become a very difficult task, in which, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, there is a need to find effective classifier member selection methods. In this paper, a DCS (Dynamic Classifier Selection)-based method is presented, which takes into account performance and diversity of the classifiers in order to choose the ensemble members.

[1]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[2]  Kevin W. Bowyer,et al.  Combination of multiple classifiers using local accuracy estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

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

[5]  Amanda J. C. Sharkey,et al.  Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .

[6]  Amanda J. C. Sharkey,et al.  Multi-Net Systems , 1999 .

[7]  Fabio Roli,et al.  Dynamic classifier selection based on multiple classifier behaviour , 2001, Pattern Recognit..

[8]  Fabio Roli,et al.  Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..

[9]  Zoran Obradovic,et al.  Effective pruning of neural network classifier ensembles , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[10]  Fabio Roli,et al.  An approach to the automatic design of multiple classifier systems , 2001, Pattern Recognit. Lett..

[11]  Ludmila I. Kuncheva,et al.  Switching between selection and fusion in combining classifiers: an experiment , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[12]  David Zhang,et al.  Face recognition by combining several algorithms , 2002, Object recognition supported by user interaction for service robots.

[13]  Matti Aksela,et al.  Comparison of Classifier Selection Methods for Improving Committee Performance , 2003, Multiple Classifier Systems.

[14]  Marc Parizeau,et al.  Flexible multi-classifier architecture for face recognition systems , 2003 .

[15]  Lawrence O. Hall,et al.  A New Ensemble Diversity Measure Applied to Thinning Ensembles , 2003, Multiple Classifier Systems.

[16]  Zhengxin Chen,et al.  Recognition of exon/intron boundaries using dynamic ensembles , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[17]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[18]  Luc Vandendorpe,et al.  Decision Fusion for Face Authentication , 2004, ICBA.

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

[20]  Giorgio Valentini,et al.  An experimental bias-variance analysis of SVM ensembles based on resampling techniques , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Terry Windeatt,et al.  Diversity measures for multiple classifier system analysis and design , 2004, Inf. Fusion.

[22]  Mykola Pechenizkiy,et al.  Diversity in search strategies for ensemble feature selection , 2005, Inf. Fusion.

[23]  Anne M. P. Canuto,et al.  Using Accuracy and Diversity to Select Classifiers to Build Ensembles , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.