A Decision-Based Dynamic Ensemble Selection Method for Concept Drift

Abstract--We propose an online method for concept drift detection based on dynamic classifier ensemble selection. T he proposed method generates a pool of ensembles by promoting diversity among classifier members and chooses expert ensem bles according to global prequential accuracy values. Unlike cu rrent dynamic ensemble selection approaches that use only local k nowl-edge to select the most competent ensemble for each instance, our method focuses on selection taking into account the deci sion space. Consequently, it is well adapted to the context of dri ft detection in data stream problems. The results of the experi ments show that the proposed method attained the highest detectio n precision and the lowest number of false alarms, besides compet itive classification accuracy rates, in artificial datasets repre senting different types of drifts. Moreover, it outperformed basel ines in different real-problem datasets in terms of classification accuracy. Practical tasks, such as identification of customer preferences, Internet log analysis, among others, are examples of data stream problems. In this context, the so-called concep t drift phenomenon may occur, since when data are continuousl y generated in streams, data and target concepts may change over time. Algorithms designed to deal with drift may be divided into two main groups: (1) online - when one instance is learned at a time upon arrival; and (2) block-based - when chunks of samples are presented from time to time [1]. Online methods are very useful in data stream environments, especially due to three main reasons: samples arrive sequential ly; data usually must be processed in high volumes at fast paces; and each data instance is read only once. Different categories of online methods are available in the literature.