期刊
NEUROCOMPUTING
卷 185, 期 -, 页码 113-132出版社
ELSEVIER
DOI: 10.1016/j.neucom.2015.12.046
关键词
Memetic algorithm; Principal component analysis; Adaptation; Parameter control; Support vector machine; Training data
资金
- Polish Ministry of Science and Higher Education [IP2012 026372]
Support vector machines (SVMs) are one of the most popular and powerful machine learning techniques, but suffer from a significant drawback of the high time and memory complexities of their training. This issue needs to be endured especially in the case of large and noisy datasets. In this paper, we propose a new adaptive memetic algorithm (PCA(2)MA) for selecting valuable SVM training data from the entire set. It helps improve the classifier score, and speeds up the classification process by decreasing the number of support vectors. In PCA(2)MA, a population of reduced training sets undergoes the evolution, which is complemented by the refinement procedures. We propose to exploit both a priori information about the training set extracted using the data geometry analysis and the knowledge attained dynamically during the PCA(2)MA execution to enhance the refined sets. Also, we introduce a new adaptation scheme to control the pivotal algorithm parameters on the fly, based on the current search state. Extensive experimental study performed on benchmark, real-world, and artificial datasets dearly confirms the efficacy and convergence capabilities of the proposed approach. We demonstrate that PCA(2)MA is highly competitive compared with other state-of-the-art techniques. (C) 2015 Elsevier B.V. All rights reserved.
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