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

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

Abstract— 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:< Final Year Projects > 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. Introduction Many factors affect the success of machine learning on a given task. The quality of the data is on…