Correlation-based Feature Selection for Machine Learning

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.

(Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural da tasets. Three machine learn-

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  Feature, Based, Machine, Selection, Learning, Correlations, Correlation based feature selection for machine learning, Correlation based feature selection

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