Hall, M. A. & Smith, L. A. (1998). Practical feature subset selection for machine learning. In C. McDonald(Ed.), Computer Science ’98 Proceedings of the 21st Australasian Computer Science Conference ACSC’98, Perth, 4-6 February, 1998(pp. 181-191). Berlin: Springer.
Permanent Research Commons link: http://hdl.handle.net/10289/1512
Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithms
This is an author’s version of an article has been published in Computer Science ’98 Proceedings of the 21st Australasian Computer Science Conference ACSC’98, Perth, 4-6 February, 1998. © Springer.