Interpretable support vector machines for functional data
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.
interpretability classification data mining linear programming regularization methods functional data analysis