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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), motivat...ing 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.続きを見る
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