Support vector machines within a bivariate mixed-integer linear programming framework

Support vector machines (SVMs) are a powerful machine learning paradigm, performing supervised learning for classification and regression analysis. A number of SVM models in the literature have made use of advances in mixed-integer linear programming (MILP) techniques in order to perform this task e...

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Vydané v:Expert systems with applications Ročník 245; s. 122998
Hlavní autori: Warwicker, John Alasdair, Rebennack, Steffen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.07.2024
Predmet:
Feature selection
Mixed-integer linear programming
Optimisation
Outlier detection
Support vector machine
Outlier detection
Feature selection
Support vector machine
Optimisation
Mixed-integer linear programming
ISSN:0957-4174, 1873-6793
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Popis
Shrnutí:Support vector machines (SVMs) are a powerful machine learning paradigm, performing supervised learning for classification and regression analysis. A number of SVM models in the literature have made use of advances in mixed-integer linear programming (MILP) techniques in order to perform this task efficiently. In this work, we present three new models for SVMs that make use of piecewise linear (PWL) functions. This allows effective separation of data points where a simple linear SVM model may not be sufficient. The models we present make use of binary variables to assign data points to SVM segments, and hence fit within a recently presented framework for machine learning MILP models. Alongside presenting an inbuilt feature selection operator, we show that the models can benefit from robust inbuilt outlier detection. Experimental results show when each of the presented models is effective, and we present guidelines on which of the models are preferable in different scenarios. •We present three new models for support vector machines using piecewise linear functions.•We show how outlier detection and feature selection can be implemented in the models.•We show when each of the models is advantageous across ad-hoc and real-world data sets.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.122998