A Kind of Combination Feature Division and Diversity Measure of Multi-Classifier Selective Ensemble Algorithm

Abstract:

In ensemble learning, in order to improve the performance of individual classifiers and the diversity of classifiers, from the classifiers generation and combination, this paper proposes a kind of combination feature division and diversity measure of multi-classifier selective ensemble algorithm. The algorithm firstly applied bagging method to create some feature subsets, Secondly using principal component analysis of feature extraction method on each feature subsets, then select classifiers with high-classification accuracy; finally before classifier combination we use classifier diversity measure method select diversity classifiers. Experimental results prove that classification accuracy of the algorithm is obviously higher than popular bagging algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 63-64)
• Pages: 55-58
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.63-64.55
• Online since: June 2011
• Authors: Yan Wang, Xiu Xia Wang, Sheng Lai
• Keywords: Diversity Measure, Feature Extraction, Multi-Classifiers Ensemble, Selective Ensemble

References

[1] Bryll, R., Gutierrez, O.R., Quek, F.: Attribute bagging: Improving accuracy of classifier ensembles by using random features subsets. J. Pattern Recognition Letters. 3 (6): 1291-1302 (2003).

DOI: 10.1016/s0031-3203(02)00121-8

[2] Zhou, Z.H., wu, J., Tang, W.: Ensembling neural networks: many could be better than all. J. Artificial Intelligence. 137, 239-263(2002).

DOI: 10.1016/s0004-3702(02)00190-x

[3] Breiman, L.: Bagging predictors. J. Machine Learning. 24, 123-140(1996).

[4] Krogh, A., Vedelsby, J.: Neural network ensembles, cross Validation active learning. C. /Tesauro, G., Touretzky, D., Leen T.: Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, 7, 231(1995).

[5] Shipp, C.A., Kuncheva, L.I.: Relationships between combination methods and measures of diversity in combining classifiers Information Fusion. J. 3: 135-148(2002).

DOI: 10.1016/s1566-2535(02)00051-9

[6] Zhu, B.Z.: Feature Extraction-based Selective Neural Network Ensemble Method. J. Computer science. 35, 132-133(2008).

[7] Chen, K.: The Study for Diversity of Ensemble Learning Based on Cluster Technology. J. Journal of Nanjing Institute of Industry Technology. 8, 22-24(2008).

[8] Brow G, Wyatt J, Harris R, Yao X. Diversity Creation Methods: A Survey and Categorisation. J. Journal of Information Fution. 1, 1-28(2005).