Feature Selection by Tree Search of Correlation-Adjusted Class Distances
نویسندگان
چکیده
The rapidly growing dimensionality of datasets has made feature selection indispensable. We introduce the TS-CACD feature-selection algorithm, which uses a generalization of the Stern-Brocot tree to traverse the search space. This family of trees supports different divergence ratios, i.e., enables the search to focus on and reach certain areas of interest more quickly. TS-CACD uses a continuous filter method, which combines an inter/intra-class distance measure with a pair-wise ranked feature correlation measure. It requires almost no parameters, explicitly selects the most important features, and performs well.
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