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Interpretable support vector machines for functional data

Belen Martin-Barragan, Rosa Lillo and Juan Romo

European Journal of Operational Research, 2014, vol. 232, issue 1, 146-155

Abstract: Support Vector Machines (SVMs) is known to be a powerful nonparametric classification technique even for high-dimensional data. Although predictive ability is important, obtaining an easy-to-interpret classifier is also crucial in many applications. Linear SVM provides a classifier based on a linear score. In the case of functional data, the coefficient function that defines such linear score usually has many irregular oscillations, making it difficult to interpret.

Keywords: Data mining; Interpretability; Classification; Linear programming; Regularization methods; Functional data analysis (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:232:y:2014:i:1:p:146-155

DOI: 10.1016/j.ejor.2012.08.017

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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