期刊
PATTERN RECOGNITION
卷 83, 期 -, 页码 91-106出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.05.012
关键词
Feature selection; Kernel partial least square; Regression coefficients; Relevance; Classification
资金
- MHRD as Centre of Excellence on Machine Learning Research and Big Data Analysis
- DST-UKEIRI Project [DST/INT/UK/P-91/2014]
Maximum relevance and minimum redundancy (mRMR) has been well recognised as one of the best feature selection methods. This paper proposes a Kernel Partial Least Square (KPLS) based mRMR method, aiming for easy computation and improving classification accuracy for high-dimensional data. Experiments with this approach have been conducted on seven real-life datasets of varied dimensionality and number of instances, with performance measured on four different classifiers: Naive Bayes, Linear Discriminant Analysis, Random Forest and Support Vector Machine. Experimental results have exhibited the advantage of the proposed method over several competing feature selection techniques. (C) 2018 Elsevier Ltd. All rights reserved.
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