Deformation of log-likelihood loss function for multiclass boosting

• Authors: Takafumi Kanamori
• Venue: Neural Networks
• Year: 2010

The purpose of this paper is to study loss functions in multiclass classification. In classification problems, the decision function is estimated by minimizing an empirical loss function, and then, the output label is predicted by using the estimated decision function. We propose a class of loss functions which is obtained by a deformation of the log-likelihood loss function. There are four main reasons why we focus on the deformed log-likelihood loss function: (1) this is a class of loss functions which has not been deeply investigated so far, (2) in terms of computation, a boosting algorithm with a pseudo-loss is available to minimize the proposed loss function, (3) the proposed loss functions provide a clear correspondence between the decision functions and conditional probabilities of output labels, (4) the proposed loss functions satisfy the statistical consistency of the classification error rate which is a desirable property in classification problems. Based on (3), we show that the deformed log-likelihood loss provides a model of mislabeling which is useful as a statistical model of medical diagnostics. We also propose a robust loss function against outliers in multiclass classification based on our approach. The robust loss function is a natural extension of the existing robust loss function for binary classification. A model of mislabeling and a robust loss function are useful to cope with noisy data. Some numerical studies are presented to show the robustness of the proposed loss function. A mathematical characterization of the deformed log-likelihood loss function is also presented.