Adaptive Cost-sensitive Online Classification

TL;DR

This paper introduces adaptive, cost-sensitive online classification algorithms that incorporate second-order information and sketching techniques, improving prediction accuracy and computational efficiency in real-world anomaly detection tasks.

Contribution

It proposes novel cost-sensitive online classifiers with adaptive regularization and sketching, enhancing performance and speed over existing first-order methods.

Findings

Algorithms outperform existing methods in accuracy.

Sketching accelerates computation with minimal performance loss.

Effective in real-world anomaly detection applications.

Abstract

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, for better trade off between the performance and efficiency, we further introduce the sketching technique into our algorithms, which significantly accelerates the computational speed with quite slight performance loss. Finally, we apply our algorithms to tackle several online anomaly detection tasks from real world. Promising results prove that the proposed algorithms are effective and efficient in solving cost-sensitive online classification problems in various real-world domains.