Information Theoretic Feature Crediting in Multiclass Support Vector Machines
Identifying relevant features for a classification task is an important issue in machine learning. In this paper, we present a feature crediting scheme for multiclass pattern recognition tasks, that utilizes the ability of Support Vector Machines to generalize well in high dimensional feature spaces. Support Vector learning identifies a small subset of training data relevant for the classification task. They primarily tackle the binary classification problem. This scheme uses relevant examples to identify relevant features for multi-class classification. We present, and employ for this purpose, an informationtheoretic measure of classifier performance. This measure addresses the key issue of average rate of information being delivered by the classifier. It provides immunity to sampling bias in the data and sensitivity to pattern of errors made by the classifier. Empirical results on a number of datasets suggest efficient applicability to data with a very large number of features.