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Collaborative work with linear classifier and extreme learning machine for fast text categorization

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Abstract

The bloom of Internet has made fast text categorization very essential. Generally, the popular methods have good classification accuracy but slow speed, and vice versa. This paper proposes a novel approach for fast text categorization, in which a collaborative work framework based on a linear classifier and an extreme learning machine (ELM) is constructed. The linear classifier, obtained by a modified non-negative matrix factorization algorithm, maps all documents from the original term space into the class space directly such that it performs classification very fast. The ELM, with good classification accuracy via some nonlinear and linear transformations, classifies a few of documents according to some given criteria to improve the classification quality of the total system. Experimental results show that the proposed method not only achieves good accuracy but also performs classification very fast, which improves the averaged speed by 180 % compared with its corresponding method.

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Authors and Affiliations

  1. College of Information Engineering, China Jiliang University, Hangzhou, China

    Wenbin Zheng

  2. College of Metrological Technology, China Jiliang University, Hangzhou, China

    Hong Tang

  3. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Yuntao Qian

Authors
  1. Wenbin Zheng
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  2. Hong Tang
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  3. Yuntao Qian
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Corresponding author

Correspondence to Wenbin Zheng.

Additional information

This work was supported by the 973 Program (No. 2012CB316400), and the National Natural Science Foundation of China (No. 11202202, No. 61272315 and No. 61171151), and the Natural Science Foundation of Zhejiang Province (No. Y6110147).

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Zheng, W., Tang, H. & Qian, Y. Collaborative work with linear classifier and extreme learning machine for fast text categorization. World Wide Web 18, 235–252 (2015). https://doi.org/10.1007/s11280-013-0225-5

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  • DOI: https://doi.org/10.1007/s11280-013-0225-5

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