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Automatic textual document categorization based on generalized instance sets and a metamodel

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2 Author(s)
Wai Lam ; Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, China ; Yiqiu Han

We propose a new approach to text categorization known as generalized instance set (GIS) algorithm under the framework of generalized instance patterns. Our GIS algorithm unifies the strengths of k-NN and linear classifiers and adapts to characteristics of text categorization problems. It focuses on refining the original instances and constructs a set of generalized instances. We also propose a metamodel framework based on category feature characteristics. It has a metalearning phase which discovers a relationship between category feature characteristics and each component algorithm. Extensive experiments have been conducted on two large-scale document corpora for both GIS and the metamodel. The results demonstrate that both approaches generally achieve promising text categorization performance.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:25 ,  Issue: 5 )