BAdaCost: Multi-class Boosting with Costs
<|reference_start|>BAdaCost: Multi-class Boosting with Costs: 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.<|reference_end|>
@article{fernández-baldera2024badacost:,
    title={BAdaCost: Multi-class Boosting with Costs},
    author={Antonio Fern'andez-Baldera, Jos'e M. Buenaposada, Luis Baumela},
    journal={Pattern Recognition. Volume 79, July 2018, Pages 467-479},
    year={2024},
    doi={10.1016/j.patcog.2018.02.022},
    archivePrefix={arXiv},
    eprint={2402.04465},
    primaryClass={cs.CV}
}