Unconfused Ultraconservative Multiclass Algorithms

Ugo Louche (1) , Liva Ralaivola (2)

1 LIF - Laboratoire d'informatique Fondamentale de Marseille
2 QARMA - éQuipe AppRentissage et MultimediA [Marseille]

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 by, e.g. Bylander (1994) and Blum et al. (1996): in these contributions, the proposed approaches to fight the noise revolve around a Perceptron 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 aforementioned literature. Theoretically well-founded, UMA furthermore displays very good empirical noise robustness, as evidenced by numerical simulations conducted on both synthetic and real data. Keywords: Multiclass classification, Perceptron, Noisy labels, Confusion Matrix

Mots clés: classification multiclass; Ultraconservative Additive; noise; Confusion matrix

Domaines: Apprentissage [cs.LG]

Ugo Louche, Liva Ralaivola. Unconfused Ultraconservative Multiclass Algorithms. ACML 2013 : Asian Conference on Machine Learning, Nov 2013, Canberra, Australia. pp.309-324. ⟨hal-00958761⟩