Talukdar, Upasana and Hazarika, Shyamanta M and Gan, John Q (2018) A Kernel Partial Least Square Based Feature Selection Method. Pattern Recognition, 83. pp. 91-106. DOI https://doi.org/10.1016/j.patcog.2018.05.012
Talukdar, Upasana and Hazarika, Shyamanta M and Gan, John Q (2018) A Kernel Partial Least Square Based Feature Selection Method. Pattern Recognition, 83. pp. 91-106. DOI https://doi.org/10.1016/j.patcog.2018.05.012
Talukdar, Upasana and Hazarika, Shyamanta M and Gan, John Q (2018) A Kernel Partial Least Square Based Feature Selection Method. Pattern Recognition, 83. pp. 91-106. DOI https://doi.org/10.1016/j.patcog.2018.05.012
Abstract
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 |
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Uncontrolled Keywords: | Feature Selection; Kernel Partial Least Square; Regression Coefficients; Relevance; Classification |
Subjects: | Q Science > QA Mathematics |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 14 Jun 2018 13:35 |
Last Modified: | 30 Oct 2024 17:22 |
URI: | http://repository.essex.ac.uk/id/eprint/22115 |
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Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0