Text categorization based on regularization extreme learning machine

被引:57
作者
Zheng, Wenbin [1 ,2 ]
Qian, Yuntao [1 ]
Lu, Huijuan [2 ]
机构
[1] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China
[2] China Jiliang Univ, Coll Informat Engn, Hangzhou 310018, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Text categorization; Extreme learning machine; Support vector machine; Latent semantic analysis; Regularization; REGRESSION; SELECTION;
D O I
10.1007/s00521-011-0808-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.
引用
收藏
页码:447 / 456
页数:10
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