Unconfused ultraconservative multiclass algorithms

TL;DR

This paper introduces UMA, a new multiclass learning algorithm that effectively handles noisy datasets by leveraging confusion matrices, extending previous binary-class approaches with strong theoretical and empirical results.

Contribution

The paper presents UMA, a novel multiclass algorithm that generalizes binary noise-robust methods using confusion matrices, with proven theoretical guarantees and empirical robustness.

Findings

UMA demonstrates strong noise robustness in simulations

The algorithm outperforms existing methods on synthetic data

Empirical results confirm theoretical noise tolerance

Abstract

We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago where the proposed approaches to combat the noise revolve around a Per-ceptron learning scheme fed with peculiar examples computed through a weighted average of points from the noisy training set. We propose to build upon these approaches and we introduce a new algorithm called UMA (for Unconfused Multiclass additive Algorithm) which may be seen as a generalization to the multiclass setting of the previous approaches. In order to characterize the noise we use the confusion matrix as a multiclass extension of the classification noise studied in the aforemen-tioned literature. Theoretically well-founded, UMA furthermore displays very good empirical noise robustness, as evidenced by numerical simulations conducted on both synthetic and real data.