A kernel-based centroid classifier using hypothesis margin. Issue 6 (1st November 2016)

• Record Type: Journal Article
• Authors: Li, Ximing; Ouyang, Jihong; Zhou, Xiaotang
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 28:Issue 6(2016)
• Page Start: 955
• Page End: 969
• Publication Date: 2016-11-01
• Subjects: document classification -- centroid classifier -- hypothesis margin -- kernel method; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2015.1042924
• Languages: English
• ISSNs: 0952-813X

Abstract

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.