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• W2803163459 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." @default.
• W2803163459 created "2018-06-01" @default.
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• W2803163459 date "2018-11-01" @default.
• W2803163459 modified "2023-10-16" @default.
• W2803163459 title "A Kernel Partial least square based feature selection method" @default.
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• W2803163459 doi "https://doi.org/10.1016/j.patcog.2018.05.012" @default.
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