Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a net...
A linear programming technique is introduced that jointly performs feature selection and classifier training so that a subset of features is optimally selected together with the c...
Abstract. This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting ...
Abstract. The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore,...
In this paper we propose PARTfs which adopts a supervised machine learning algorithm, namely partial decision trees, as a method for feature subset selection. In particular, it is...