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A Kernel Partial Least Square Based Feature Selection Method

Talukdar, Upasana and Hazarika, Shyamanta M and Gan, John Q (2018) 'A Kernel Partial Least Square Based Feature Selection Method.' Pattern Recognition, 83. 91 - 106. ISSN 0031-3203

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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.

Item Type: Article
Uncontrolled Keywords: Feature Selection, Kernel Partial Least Square, Regression Coefficients, Relevance, Classification
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 14 Jun 2018 13:35
Last Modified: 15 Jun 2020 13:15

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