Factorized MultiClass Boosting

TL;DR

This paper presents a novel multiclass classification method that decomposes the problem into regression tasks solved with CART trees, achieving faster performance and robustness to imbalanced datasets without sacrificing accuracy.

Contribution

Introduces a factorized boosting approach for multiclass classification that improves speed and robustness compared to existing methods.

Findings

Faster training times than state-of-the-art solutions

Maintains high model quality in imbalanced datasets

Robust to class imbalance without re-balancing techniques

Abstract

In this paper, we introduce a new approach to multiclass classification problem. We decompose the problem into a series of regression tasks, that are solved with CART trees. The proposed method works significantly faster than state-of-the-art solutions while giving the same level of model quality. The algorithm is also robust to imbalanced datasets, allowing to reach high-quality results in significantly less time without class re-balancing.