# Shedding Light on the Asymmetric Learning Capability of AdaBoost

arXiv: https://arxiv.org/abs/1507.02084 · HTML: https://mlsanity.com/paper/1507.02084/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2015.

## Built on by (most cited first, 2 of 2)

- [AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification](https://mlsanity.com/paper/2209.08309/) (2022)
- [BAdaCost: Multi-class Boosting with Costs](https://mlsanity.com/paper/2402.04465/) (2024)

## Abstract (arXiv)

In this paper, we propose a different insight to analyze AdaBoost. This analysis reveals that, beyond some preconceptions, AdaBoost can be directly used as an asymmetric learning algorithm, preserving all its theoretical properties. A novel class-conditional description of AdaBoost, which models the actual asymmetric behavior of the algorithm, is presented.