Cost-sensitive Boosting for Concept Drift

Concept drift is a phenomenon typically experienced when data distributions change continuously over a period of time. In this paper we propose a cost-sensitive boosting approach for learning under concept drift. The proposed methodology estimates relevance costs of ‘old’ data samples w.r.t. to ‘newer’ samples and integrates it into the boosting process. We experiment this methodology on usenet1 and accelerometer based activity gesture datasets. The results demonstrate that the cost-sensitive boosting approach significantly improves classification performance over existing algorithms.