Information Theory vs. Correlation Based Feature Ranking Methods in Application to Metallurgical Problem Solving

Feature selection is a typical stage of building any classification or regression model. There are several approaches to it, however one of the fastest is based on determining the relevance of each feature independently by calculating ranking values. In this paper we provide empirical comparison of four different ranking criteria that belong to two different groups information theory and correlation metrics. The comparison is performed on the empirical datasets obtained while building a model used for predicting mass of chemical compounds necessary to obtain steel of predefined quality.