Unconfused ultraconservative multiclass algorithms
This addresses noise robustness in multiclass classification, but it is incremental as it builds upon existing two-class methods.
The paper tackles the problem of learning linear classifiers from noisy datasets in a multiclass setting by introducing UMA, a generalization of previous two-class approaches, which shows strong empirical noise robustness in simulations on synthetic and real data.
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.