RandomBoost: Simplified Multi-class Boosting through Randomization

TL;DR

RandomBoost introduces a simplified multi-class boosting method using random projections, reducing complexity and maintaining high accuracy across various datasets.

Contribution

The paper presents a novel multi-class boosting approach leveraging random projections, resulting in a single parameter classifier and improved efficiency.

Findings

Outperforms existing multi-class boosting algorithms in accuracy

Converges faster than traditional methods

Effective on synthetic, machine learning, and visual recognition datasets

Abstract

We propose a novel boosting approach to multi-class classification problems, in which multiple classes are distinguished by a set of random projection matrices in essence. The approach uses random projections to alleviate the proliferation of binary classifiers typically required to perform multi-class classification. The result is a multi-class classifier with a single vector-valued parameter, irrespective of the number of classes involved. Two variants of this approach are proposed. The first method randomly projects the original data into new spaces, while the second method randomly projects the outputs of learned weak classifiers. These methods are not only conceptually simple but also effective and easy to implement. A series of experiments on synthetic, machine learning and visual recognition data sets demonstrate that our proposed methods compare favorably to existing multi-class boosting algorithms in terms of both the convergence rate and classification accuracy.