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Feature Selection Algorithm Using Relative Odds for Data Mining Classification

Feature Selection Algorithm Using Relative Odds for Data Mining Classification

Donald Douglas Atsa'am
Copyright: © 2020 |Pages: 26
ISBN13: 9781522597506|ISBN10: 1522597506|EISBN13: 9781522597520
DOI: 10.4018/978-1-5225-9750-6.ch005
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MLA

Atsa'am, Donald Douglas. "Feature Selection Algorithm Using Relative Odds for Data Mining Classification." Big Data Analytics for Sustainable Computing, edited by Anandakumar Haldorai and Arulmurugan Ramu, IGI Global, 2020, pp. 81-106. https://doi.org/10.4018/978-1-5225-9750-6.ch005

APA

Atsa'am, D. D. (2020). Feature Selection Algorithm Using Relative Odds for Data Mining Classification. In A. Haldorai & A. Ramu (Eds.), Big Data Analytics for Sustainable Computing (pp. 81-106). IGI Global. https://doi.org/10.4018/978-1-5225-9750-6.ch005

Chicago

Atsa'am, Donald Douglas. "Feature Selection Algorithm Using Relative Odds for Data Mining Classification." In Big Data Analytics for Sustainable Computing, edited by Anandakumar Haldorai and Arulmurugan Ramu, 81-106. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9750-6.ch005

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Abstract

A filter feature selection algorithm is developed and its performance tested. In the initial step, the algorithm dichotomizes the dataset then separately computes the association between each predictor and the class variable using relative odds (odds ratios). The value of the odds ratios becomes the importance ranking of the corresponding explanatory variable in determining the output. Logistic regression classification is deployed to test the performance of the new algorithm in comparison with three existing feature selection algorithms: the Fisher index, Pearson's correlation, and the varImp function. A number of experimental datasets are employed, and in most cases, the subsets selected by the new algorithm produced models with higher classification accuracy than the subsets suggested by the existing feature selection algorithms. Therefore, the proposed algorithm is a reliable alternative in filter feature selection for binary classification problems.

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