Correlation-based Feature Selection for Machine Learning

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Department of Computer ScienceHamilton, NewZealandCorrelation- based Feature Selection forMachine LearningMark A. HallThis thesis is submitted in partial fulfilment of the requirementsfor the degree of Doctor of Philosophy at The University of 1999c 1999 Mark A. HalliiAbstractA central problem in Machine Learning is identifying a representative set of features fromwhich to construct a classification model for a particular task. This thesis addresses theproblem of Feature Selection for Machine Learning through acorrelation based central hypothesis is that good Feature sets contain features that are highly correlatedwith the class, yet uncorrelated with each other. A Feature evaluation formula, basedon ideas from test theory, provides an operational definition of this hypothesis.

A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular ta sk. This thesis addresses the problem of feature selection for machine learning through a correlation based approach.

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