Feature Selection For Machine Learning: Comparing a Correlation-based Filter Approach to the Wrapper

May 1999

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

Volume

Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)

All Papers

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 filter 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 filter. This paper describes a new filter 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.