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Article

A Feature Selection Algorithm Performance Metric for Comparative Analysis

1
Department of Industrial Engineering, Division of Computer Science, Stellenbosch University, Stellenbosch 7600, South Africa
2
Department of Decision Sciences, University of South Africa, Pretoria 7701, South Africa
*
Author to whom correspondence should be addressed.
Academic Editor: Frank Werner
Algorithms 2021, 14(3), 100; https://doi.org/10.3390/a14030100
Received: 29 November 2020 / Revised: 5 March 2021 / Accepted: 19 March 2021 / Published: 22 March 2021
This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems. The baseline fitness improvement (BFI) measure quantifies the potential value gained by applying feature selection. The BFI measure can be used to compare the performance of feature selection algorithms across datasets by measuring the change in classifier performance as a result of feature selection, with respect to the baseline where all features are included. Empirical results are presented to show that there is performance complementarity for a suite of feature selection algorithms on a variety of real world datasets. The BFI measure is a normalised performance metric that can be used to correlate problem characteristics with feature selection algorithm performance, across multiple datasets. This ability paves the way towards describing the performance space of the per-instance algorithm selection problem for feature selection algorithms. View Full-Text
Keywords: feature selection; baseline fitness improvement; performance analysis feature selection; baseline fitness improvement; performance analysis
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MDPI and ACS Style

Mostert, W.; Malan, K.M.; Engelbrecht, A.P. A Feature Selection Algorithm Performance Metric for Comparative Analysis. Algorithms 2021, 14, 100. https://doi.org/10.3390/a14030100

AMA Style

Mostert W, Malan KM, Engelbrecht AP. A Feature Selection Algorithm Performance Metric for Comparative Analysis. Algorithms. 2021; 14(3):100. https://doi.org/10.3390/a14030100

Chicago/Turabian Style

Mostert, Werner, Katherine M. Malan, and Andries P. Engelbrecht. 2021. "A Feature Selection Algorithm Performance Metric for Comparative Analysis" Algorithms 14, no. 3: 100. https://doi.org/10.3390/a14030100

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