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| One-Class Classifier Algorithm Based on Ensemble Multi-Spanning Trees by Pruning Random Subspace Method |
| HU Zheng-Ping,LIU Kai |
| College of Information Science and Engineering,Yanshan University,Qinhuangdao 066004 |
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Abstract Due to the redundancy and the noise in high-dimensional data,a covering model constructed from these data can not reflect their distribution information,which leads to the performance degradation of one-class classifiers. To solve this problem,a pruning random subspace ensemble multi-spanning tree method is proposed. Firstly,several random subspaces are created,and minimum spanning tree covering models are constructed in each subspace respectively. Next,pruning ensembles are applied to each classifier by using an evaluation criterion. Finally,these subspace classifiers are integrated into an ensemble classifier by mean combining. Experimental results show that the proposed covering classifier by ensemble multi-trees has a better correct rate in classification than other direct covering classifiers and bagging algorithm.
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Received: 15 March 2012
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2013, Vol. 26 
