1. Integration of an improved dynamic ensemble selection approach to enhance one-vs-one scheme.
- Author
-
Zhang, Zhong-Liang, Luo, Xing-Gang, Yu, Yang, Yuan, Bo-Wen, and Tang, Jia-Fu
- Subjects
- *
FACE perception , *DATA mining , *MACHINE learning , *SUPPORT vector machines , *STATISTICAL ensembles - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF