An Optimization Method for Characterizing Two Groups of Data

20 Pages Posted: 7 Apr 2020

See all articles by Amir Salehipour

Amir Salehipour

University of Technology Sydney, Australia; University of Technology Sydney (UTS)

Date Written: March 13, 2020

Abstract

Feature selection is to choose a subset of features, out of a set of candidate features, such that the selected set best represents the whole in a particular aspect. We present a bi-objective optimization model for a feature selection problem in the context of data grouping. The aim is to select a set of features that has the smallest size and maximizes the similarities between samples of the same group and the differences between samples of different groups. We propose a lexicographic solution method and prove several properties of the problem. We show that even obtaining feasible solutions for the problem can be challenging, and we therefore develop efficient matheuristic algorithms. We test our algorithms on 136 datasets ranging from medium to large, including 11 real-world ones. We show that the proposed matheuristics can deliver quality solutions in a reasonable amount of time.

Keywords: optimization, bi-objective, matheuristics

Suggested Citation

Salehipour, Amir and Salehipour, Amir, An Optimization Method for Characterizing Two Groups of Data (March 13, 2020). Available at SSRN: https://ssrn.com/abstract=3553714 or http://dx.doi.org/10.2139/ssrn.3553714

Amir Salehipour (Contact Author)

University of Technology Sydney (UTS) ( email )

15 Broadway, Ultimo
PO Box 123
Sydney, NSW 2007
Australia

University of Technology Sydney, Australia ( email )

Ultimo
Ultimo, NSW 2007
Australia

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