Pruning Training Samples Using a Supervised Clustering Algorithm

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. Key words: Supervised clustering, Min-max modular network, Gaussian-zerocrossing function, Patent classification, Training sample pruning

• Type: Journal
• Year: 2010
• Where: ISNN
• Authors: Minzhang Huang, Hai Zhao, Bao-Liang Lu
• Keywords: Classification Tasks, ISNN 2010, Min-max Modular Network, Neural Networks, Patent Classification