XML document classification based on ELM |
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Authors: | Xiang-guo ZhaoAuthor Vitae Guoren WangAuthor Vitae Xin BiAuthor Vitae Peizhen Gong Yuhai Zhao |
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Affiliation: | a Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education, China b College of Information Science and Engineering, Northeastern University, Liaoning, Shenyang 110004, China |
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Abstract: | In this paper, we describe an XML document classification framework based on extreme learning machine (ELM). On the basis of Structured Link Vector Model (SLVM), an optimized Reduced Structured Vector Space Model (RS-VSM) is proposed to incorporate structural information into feature vectors more efficiently and optimize the computation of document similarity. We apply ELM in the XML document classification to achieve good performance at extremely high speed compared with conventional learning machines (e.g., support vector machine). A voting-ELM algorithm is then proposed to improve the accuracy of ELM classifier. Revoting of Equal Votes (REV) method and Revoting of Confusing Classes (RCC) method are also proposed to postprocess the voting result of v-ELM and further improve the performance. The experiments conducted on real world classification problems demonstrate that the voting-ELM classifiers presented in this paper can achieve better performance than ELM algorithms with respect to precision, recall and F-measure. |
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Keywords: | XML Classification Extreme learning machine Structure Link Vector Model |
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