A Local-branching heuristic for the best subset selection problem in linear regression

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)


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 and Strub, Oliver


600 Technology > 650 Management & public relations




Juliana Kathrin Moser-Zurbrügg

Date Deposited:

22 Jan 2019 11:35

Last Modified:

28 Jan 2020 13:49





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