A kernel-based centroid classifier using hypothesis margin

作者：Li, Ximing; Ouyang, Jihong^*; Zhou, Xiaotang

来源：Journal of Experimental & Theoretical Artificial Intelligence, 2016, 28(6): 955-969.

DOI：10.1080/0952813X.2015.1042924

• document classification
• centroid classifier
• hypothesis margin
• kernel method

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.

• 出版日期2016
• 单位吉林大学