BAdaCost: Multi-class Boosting with Costs

• URL: http://arxiv.org/abs/2402.04465v1
• Date: Tue, 6 Feb 2024 23:18:29 GMT
• Title: BAdaCost: Multi-class Boosting with Costs
• Authors: Antonio Fern\'andez-Baldera, Jos\'e M. Buenaposada, Luis Baumela
• Abstract summary: BAdaCost is a multi-class cost-sensitive classification algorithm. It achieves significant gains in performance when compared to previous multi-class cost-sensitive approaches.
• Score: 2.6080756513915824
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present BAdaCost, a multi-class cost-sensitive classification algorithm. It combines a set of cost-sensitive multi-class weak learners to obtain a strong classification rule within the Boosting framework. To derive the algorithm we introduce CMEL, a Cost-sensitive Multi-class Exponential Loss that generalizes the losses optimized in various classification algorithms such as AdaBoost, SAMME, Cost-sensitive AdaBoost and PIBoost. Hence unifying them under a common theoretical framework. In the experiments performed we prove that BAdaCost achieves significant gains in performance when compared to previous multi-class cost-sensitive approaches. The advantages of the proposed algorithm in asymmetric multi-class classification are also evaluated in practical multi-view face and car detection problems.

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