Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper

Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information often improves the performance of machine learning algorithms. There are two common approaches: a wrapper uses the intended learning algorithm itself to evaluate the usefulness of features, while a fllter evaluates features according to heuristics based on general characteristics of the data. The wrapper approach is generally considered to produce better feature subsets but runs much more slowly than a fllter. This paper describes a new fllter approach to feature selection that uses a correlation based heuristic to evaluate the worth of feature subsets When applied as a data preprocessing step for two common machine learning algorithms, the new method compares favourably with the wrapper but requires much less computation.