# BAdaCost: Multi-class Boosting with Costs

arXiv: https://arxiv.org/abs/2402.04465 · HTML: https://mlsanity.com/paper/2402.04465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection](https://mlsanity.com/paper/1607.07155/) (2016)
- [Filtered Channel Features for Pedestrian Detection](https://mlsanity.com/paper/1501.05759/) (2015)
- [Aggregate channel features for multi-view face detection](https://mlsanity.com/paper/1407.4023/) (2014)
- [Shedding Light on the Asymmetric Learning Capability of AdaBoost](https://mlsanity.com/paper/1507.02084/) (2015)
- [Learning to Detect Vehicles by Clustering Appearance Patterns](https://mlsanity.com/paper/1503.03771/) (2015)

## Abstract (arXiv)

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.