# THORS: An Efficient Approach for Making Classifiers Cost-sensitive

arXiv: https://arxiv.org/abs/1811.02814 · HTML: https://mlsanity.com/paper/1811.02814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Minimum Viable Model Estimates for Machine Learning Projects](https://mlsanity.com/paper/2101.00346/) (2021)

## Abstract (arXiv)

In this paper, we propose an effective THresholding method based on 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 order statistic to find an optimal threshold for classification, requiring almost no knowledge of classifiers itself. Unlike common data-driven methods, we analytically show that THORS has theoretical guaranteed performance, theoretical bounds for the costs and lower time complexity. Coupled with empirical results on several real-world data sets, we argue that THORS is the preferred cost-sensitive technique.