Pruning training samples using a supervised clustering algorithm

Authors:
Minzhang Huang;Hai Zhao;Bao-Liang Lu
Affiliations:
Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University;Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University;Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

As practical pattern classification tasks are often very-large scale and serious imbalance such as patent classification, using traditional pattern classification techniques in a plain way to deal with these tasks has shown inefficient and ineffective In this paper, a supervised clustering algorithm based on min-max modular network with Gaussian-zero-crossing function is adopted to prune training samples in order to reduce training time and improve generalization accuracy The effectiveness of the proposed training sample pruning method is verified on a group of real patent classification tasks by using support vector machines and nearest neighbor algorithm.