Soft Methodology for Cost-and-error Sensitive Classification

TL;DR

This paper introduces a soft cost-sensitive classification methodology that balances cost minimization and error rate, improving practical performance and extending to weighted errors for unbalanced data.

Contribution

It proposes a novel multicriteria optimization approach that regularizes cost-sensitive classification with error rate, enhancing existing algorithms and addressing unbalanced classification issues.

Findings

Achieves lower test error rates on benchmark and real-world datasets.

Maintains similar or lower test costs compared to existing algorithms.

Extends to weighted error rate for unbalanced classification problems.

Abstract

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms. We also demonstrate that the methodology can be extended for considering the weighted error rate instead of the original error rate. This extension is useful for tackling unbalanced classification problems.