Abrupt Change Detection with One-Class Time-Adaptive Support Vector Machines

G.L. GRINBLAT; L.C. UZAL; P. M. GRANITTO

EXPERT SYSTEMS WITH APPLICATIONS

PERGAMON-ELSEVIER SCIENCE LTD

Lugar: Amsterdam; Año: 2013 vol. 40 p. 7242 - 7249

0957-4174

We recently introduced an algorithm for training a sequence of coupled Support Vector Machines which shows promising results in the field of non-stationary classification problems [12]. In this paper we analyze its application to the abrupt change detection problem. With this goal, we first introduce and analyze an extension of it to deal with the One-Class Support Vector Machine (OC-SVM) problem, and then discuss its use as an improved abrupt change detection method. Finally, we apply the proposed procedure to artificial and real-world examples, and demonstrate that it is competitive by comparison against other abrupt change detection methods.