  Feature selection is an important pre-processing step for many pattern
classification tasks. Traditionally, feature selection methods are designed to
obtain a feature subset that can lead to high classification accuracy. However,
classification accuracy has recently been shown to be an inappropriate
performance metric of classification systems in many cases. Instead, the Area
Under the receiver operating characteristic Curve (AUC) and its multi-class
extension, MAUC, have been proved to be better alternatives. Hence, the target
of classification system design is gradually shifting from seeking a system
with the maximum classification accuracy to obtaining a system with the maximum
AUC/MAUC. Previous investigations have shown that traditional feature selection
methods need to be modified to cope with this new objective. These methods most
often are restricted to binary classification problems only. In this study, a
filter feature selection method, namely MAUC Decomposition based Feature
Selection (MDFS), is proposed for multi-class classification problems. To the
best of our knowledge, MDFS is the first method specifically designed to select
features for building classification systems with maximum MAUC. Extensive
empirical results demonstrate the advantage of MDFS over several compared
feature selection methods.
