Paper Info
Title
DRCW-FRkNN-OVO: distance-based related competence weighting based on fixed radius k nearest neighbour for one-vs-one scheme
Abstract
The one-versus-one (OVO) binarization decomposition scheme is considered as one of the most effective techniques to deal with multi-class classification problems. Its inherent mechanism is to use the “divide-and-conquer” strategy to decompose the multi-class classification problem into as many pairs of easier-to-solve binary sub-problems as possible. One common issue in the OVO scheme is that of non-competent classifiers. In this study, we proposed a novel OVO scheme strategy, named DRCW-FRkNN-OVO, to reduce the negative effect of non-competent classifiers. Specifically, we focused on the definition of region of competence, which plays a crucial role in managing the non-competent classifiers. To overcome the issue of skew and sparse distribution during the management of non-competent classifiers, we developed a relative competence weighting combination method via the fixed radius nearest neighbour search to find the local region within each class for the query sample. Our proposed DRCW-FRkNN-OVO is tested on 30 real-world multi-class datasets compared with several well-known related works. Experimental results supported by thorough statistical analysis confirmed the effectiveness and robustness of our proposed method.
Year
DOI
Venue
2022
10.1007/s13042-021-01458-7
International Journal of Machine Learning and Cybernetics
Keywords
DocType
Volume
Multi-class classification, Binarization decomposition, One-versus-one, Non-competent classifier, Pairwise learning, Dynamic selection
Journal
13
Issue
ISSN
Citations 
5
1868-8071
0
PageRank 
References 
Authors
0.34
26
3
Name
Order
Citations
PageRank
Zhongliang Zhang1142.02
Xing-Gang Luo213814.85
Qing Zhou330.74