Correlation-based Feature Selection using Evolutionary Programming

M. Sadeghzadeh and M.H. Shenasa (Iran)


Feature selection, Evolutionary programming


Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive affect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. This paper describes a correlation-based algorithm using evolutionary programming for classification problems to determine the goodness of feature subsets, and evaluates its effectiveness with some common machine learning algorithms.

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