Bigler, Tamara; Strub, Oliver (17 December 2018). A Local-branching heuristic for the best subset selection problem in linear regression. In: IEEM 2018: IEEE International Conference on Industrial Engineering and Engineering Management. Bangkok. 16.-19.12.2018.
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The best subset selection problem in linear regression consists of selecting a small subset with a given maximum cardinality of a set of features, i.e explanatory variables, to build a linear regression model that is able to explain a given set of observations of a response
variable as exactly as possible. The motivation in building linear regression models that include only a small number of features is that these models are easier to interpret. In this paper, we present a heuristic based on the concept of local branching. Such a heuristic
repeatedly performs local-search iterations by applying mixed-integer programming. In each local-search iteration, we consider a different randomly selected subset of the features to reduce the required computational time. The results of our computational tests demonstrate that the proposed local-branching heuristic delivers better linear regression models than a pure mixed-integer programming approach within a limited amount of
computational time.
Item Type: |
Conference or Workshop Item (Paper) |
|---|---|
Division/Institute: |
03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Financial Management > Professorship for Quantitative Methods in Business Administration |
UniBE Contributor: |
Bigler, Tamara, Strub, Oliver |
Subjects: |
600 Technology > 650 Management & public relations |
Language: |
English |
Submitter: |
Juliana Kathrin Moser-Zurbrügg |
Date Deposited: |
22 Jan 2019 11:35 |
Last Modified: |
05 Dec 2022 15:24 |
BORIS DOI: |
10.7892/boris.123484 |
URI: |
https://boris.unibe.ch/id/eprint/123484 |
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