Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Abstract

Machine learning has received increased interest by both the scientific community and the industry. Most of the machine learning algorithms rely on certain distance metrics that can only be applied to numeric data. This becomes a problem in complex datasets that contain heterogeneous data consisted of numeric and nominal (i.e. categorical) features. Thus the need of transformation from nominal to numeric data. Weight of evidence (WoE) is one of the parameters that can be used for transformation of the nominal features to numeric. In this paper we describe a method that uses WoE to transform the features. Although the applicability of this method is researched to some extent, in this paper we extend its applicability for multi-class problems, which is a novelty. We compared it with the method that generates dummy features. We test both methods on binary and multi-class classification problems with different machine learning algorithms. Our experiments show that the WoE based transformation generates smaller number of features compared to the technique based on generation of dummy features while also improving the classification accuracy, reducing memory complexity and shortening the execution time. Be that as it may, we also point out some of its weaknesses and make some recommendations when to use the method based on dummy features generation instead.