Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/0952813X.2015.1042924 | JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
document classification,centroid classifier,hypothesis margin,kernel method | Kernel (linear algebra),Feature vector,Radial basis function,Margin (machine learning),Pattern recognition,Computer science,Minimisation (psychology),Artificial intelligence,Kernel method,Classifier (linguistics),Machine learning,Centroid | Journal |
Volume | Issue | ISSN |
28.0 | 6 | 0952-813X |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ximing Li | 1 | 44 | 13.97 |
| Jihong OuYang | 2 | 94 | 15.66 |
| Xiaotang Zhou | 3 | 19 | 4.08 |