A mixed-integer exponential cone programming formulation for feature subset selection in logistic regression

Asgharieh Ahari, Sahand and Kocuk, Burak (2023) A mixed-integer exponential cone programming formulation for feature subset selection in logistic regression. EURO Journal on Computational Optimization, 11 . ISSN 2192-4406 (Print) 2192-4414 (Online)

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Abstract

Logistic regression is one of the widely-used classification tools to construct prediction models. For datasets with a large number of features, feature subset selection methods are considered to obtain accurate and interpretable prediction models, in which irrelevant and redundant features are removed. In this paper, we address the problem of feature subset selection in logistic regression using modern optimization techniques. To this end, we formulate this problem as a mixed-integer exponential cone program (MIEXP). To the best of our knowledge, this is the first time both nonlinear and discrete aspects of the underlying problem are fully considered within an exact optimization framework. We derive different versions of the MIEXP model by the means of regularization and goodness of fit measures including Akaike Information Criterion and Bayesian Information Criterion. Finally, we solve our MIEXP models using the solver MOSEK and evaluate the performance of our different versions over a set of toy examples and benchmark datasets. The results show that our approach is quite successful in obtaining accurate and interpretable prediction models compared to other methods from the literature.
Item Type: Article
Uncontrolled Keywords: Classification; Machine learning; Mixed-integer exponential cone programming; Sparse logistic regression
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Industrial Engineering
Faculty of Engineering and Natural Sciences
Depositing User: Burak Kocuk
Date Deposited: 07 Aug 2023 21:19
Last Modified: 07 Aug 2023 21:19
URI: https://research.sabanciuniv.edu/id/eprint/47577

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