Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various classes of methods, forward feature selection methods based on mutual information have become very popular and are widely used in practice. However, comparative evaluations of these methods have been limited by being based on specific datasets and classifiers. In this talk, we discuss a theoretical framework that allows evaluating the methods based on their theoretical properties. The estimation difficulties of the method’s objective functions will also be addressed.

This is a joint work with Francisco Macedo, António Pacheco, and Rui Valadas.