Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning 论文

摘要

Algorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while lters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, lters have proven to be more practical than wrappers because they are much faster. However, most existing lter algorithms only work with discrete classi cation problems. This paper describes a fast, correlation-based lter algorithm that can be applied to continuous and discrete problems. Experiments using the new method as a preprocessing step for naive Bayes, instance-based learning, decision trees, locally weighted regression, and model trees show it to be an e ective feature selector|it reduces the data in dimensionality by more than sixty percent in most cases without negatively a ecting accuracy. Also, decision and model trees built from the pre-processed data are often signi cantly smaller. 1 1