# The return of AdaBoost.MH: multi-class Hamming trees

arXiv: https://arxiv.org/abs/1312.6086 · HTML: https://mlsanity.com/paper/1312.6086/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2013.

## Built on by (most cited first, 10 of 10)

- [Multiclass Boosting: Simple and Intuitive Weak Learning Criteria](https://mlsanity.com/paper/2307.00642/) (2023)
- [Mixture of Linear Models Co-supervised by Deep Neural Networks](https://mlsanity.com/paper/2108.04035/) (2021)
- [Effective training-time stacking for ensembling of deep neural networks](https://mlsanity.com/paper/2206.13491/) (2022)
- [A Comparative Evaluation of Machine Learning Algorithms for the Prediction of R/C Buildings' Seismic Damage](https://mlsanity.com/paper/2203.13449/) (2022)
- [Classification of Buildings' Potential for Seismic Damage by Means of Artificial Intelligence Techniques](https://mlsanity.com/paper/2205.01076/) (2022)
- [Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach](https://mlsanity.com/paper/2305.10257/) (2023)
- [Active learning for fast and slow modeling attacks on Arbiter PUFs](https://mlsanity.com/paper/2308.13645/) (2023)
- [Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset](https://mlsanity.com/paper/2310.10670/) (2023)
- [Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis](https://mlsanity.com/paper/2407.14631/) (2024)
- [Factorized AdaBoost.MH Achieves the Same Convergence Rate as AdaBoost.MH](https://mlsanity.com/paper/2608.01091/) (2026)

## Abstract (arXiv)

Within the framework of AdaBoost.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to K binary one-against-all classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary AdaBoost. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLogitBoost, and it is significantly better than other known implementations of AdaBoost.MH.