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Cost-sensitive Boosting with p-norm Loss Functionsand its Applications

In practical applications of classification, there are often varying costs associated with different types of misclassification (e.g. fraud detection, anomaly detection and medical diagnosis), motivating the need for the so-called ”cost-sensitive” classification. In this paper, we introduce a family of novel boosting methods for cost-sensitive classification by applying the theory of gradient boosting to p-norm based cost functionals, and establish theoretical guarantees as well as their empirical advantage over existing algorithms.