A kernel-based centroid classifier using hypothesis margin |
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Authors: | Ximing Li Xiaotang Zhou |
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Affiliation: | 1. College of Computer Science and Technology, Jilin University, Changchun, P.R. China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, P.R. China |
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Abstract: | The centroid-based classifier is both effective and efficient for document classification. However, it suffers from over-fitting and linear inseparability problems caused by its fundamental assumptions. To address these problems, we propose a kernel-based hypothesis margin centroid classifier (KHCC). First, KHCC optimises the class centroids via minimising hypothesis margin under structural risk minimisation principle; second, KHCC uses the kernel method to relieve the problem of linear inseparability in the original feature space. Given the radial basis function, we further discuss a guideline for tuning the value of its parameter. The experimental results on four well-known data-sets indicate that our KHCC algorithm outperforms the state-of-the-art algorithms, especially for the unbalanced data-set. |
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Keywords: | document classification centroid classifier hypothesis margin kernel method |
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