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arXiv · 1506.07254

Unconfused ultraconservative multiclass algorithms

Ugo Louche·Liva Ralaivola
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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.

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BibTeXRIS

Ugo Louche, Liva Ralaivola. 2015-06-24. Unconfused ultraconservative multiclass algorithms. https://doi.org/10.1007/s10994-015-5490-3

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