Ashok Venkatesan

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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(More)
i ABSTRACT Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with(More)
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