Paper Info
Title
Integration of an improved dynamic ensemble selection approach to enhance one-vs-one scheme.
Abstract
The One-vs-One (OVO) scheme that decomposes the original more complicated problem into as many as possible pairs of easier-to-solve binary sub-problems is one of the most popular techniques for handling multi-class classification problems. In this paper, we propose an improved Dynamic Ensemble Selection (DES) procedure, which aims to enhance the OVO scheme via dynamically selecting a group of appropriate heterogeneous classifiers in each sub-problem for each query example. To do so, twenty heterogeneous classification algorithms are selected to obtain a set of candidate classifiers for each sub-problem derived from the OVO decomposition. Then, a simple yet efficient DES procedure is developed to execute the dynamic selection for each query example in each sub-problem. Finally, all the selected binary heterogeneous ensembles are combined by using majority voting to obtain the final output class. To evaluate the proposed method, we carry out a series of experiments on twenty datasets selected from the KEEL repository. The results supported by proper statistical tests demonstrate the validity and effectiveness of our proposed method, compared with state-of-the-art methods for OVO-based multi-class classification.
Year
DOI
Venue
2018
10.1016/j.engappai.2018.06.002
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Dynamic selection,Heterogeneous ensemble,One-vs-one,Decomposition strategy,Multi-class classification
Ensemble selection,Computer science,Artificial intelligence,Majority rule,Statistical classification,Statistical hypothesis testing,Machine learning,Binary number
Journal
Volume
ISSN
Citations 
74
0952-1976
2
PageRank 
References 
Authors
0.35
24
5
Name
Order
Citations
PageRank
Zhongliang Zhang1362.86
Xing-Gang Luo213814.85
Yang Yu331.04
Bo-Wen Yuan441.73
J. F. Tang5685.38