RT Journal Article
T1 Interpretable support vector machines for functional data
A1 Martin-Barragan, Belen
A1 Lillo Rodríguez, Rosa Elvira
A1 Romo, Juan
AB 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.This paper presents a new method, called Interpretable Support Vector Machines for Functional Data, that provides an interpretable classifier with high predictive power. Interpretability might be understood in different ways. The proposed method is flexible enough to cope with different notions of interpretability chosen by the user, thus the obtained coefficient function can be sparse, linear-wise, smooth, etc. The usefulness of the proposed method is shown in real applications getting interpretable classifiers with comparable, sometimes better, predictive ability versus classical SVM.
PB Elsevier
SN 0377-2217
YR 2014
FD 2014-01-01
LK https://hdl.handle.net/10016/33291
UL https://hdl.handle.net/10016/33291
LA eng
NO The authors thank the anonymous referees and the associate editor for their helpful comments to improve the article. This work has been partially supported by projects MTM2009-14039, ECO2011-25706 of Ministerio de Ciencia e Innovación and FQM-329 of Junta de Andalucía, Spain.
DS e-Archivo
RD 27 jul. 2024