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THORS: An Efficient Approach for Making Classifiers Cost-Sensitive
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DOI：10.1109/ACCESS.2019.2929078.png)
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Abstract
En 中文
In this paper, we propose an effective THresholding method based on the Order Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one. The procedure uses the order statistic to find an optimal threshold for classification, requiring almost no knowledge of the classifier itself. Unlike common data-driven methods, we analytically show that the THORS has theoretical guaranteed performance, theoretical bounds for the costs, and low-time complexity. Coupled with empirical results on several real-world data sets, we argue that the THORS is the preferred cost-sensitive learning technique.
Keywords:
Cost-sensitive
order statistic
scoring classifier
thresholding
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