Building a time variant cost-oriented classifier using an ensemble of SVMs on a real case application

This paper shows an attempt to build a time variant cost-oriented classifier for two-class classification problems. Such classifier is based on a sliding window, and has been designed as an ensemble of Cost-Oriented Support Vector Machines (CO-SVMs). More precisely, we have integrated the Incremental/Decremental (ID) formulation of SVMs with the Cost-Oriented (CO) formulation, thus obtaining an ensemble of COID-SVMs. At each data arrival, the new pattern is classified by using a dynamic selection of the underlying COID-SVMs in the Receiver Operating Characteristic (ROC) space by means of the ROC convex hull method. Then, once the actual class label of the new pattern is known, the new data and the associated class label are used to perform an incremental learning by each COID-SVM. At the same time, each SVM is updated by performing the decremental learning of the data falling outside the sliding window. This allows to adapt the classification to time varying conditions. The methodology has been applied to the classification of oil spills at sea from remotely sensed optical images.