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dc.contributor.authorGrzymala-Busse, Jerzy W.
dc.contributor.authorHippe, Zdzislaw S.
dc.contributor.authorMroczek, Teresa
dc.identifier.citationGrzymala-Busse, J.W.; Hippe, Z.S.; Mroczek, T. Reduced Data Sets and Entropy-Based Discretization. Entropy 2019, 21, 1051.en_US
dc.descriptionThis work is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.description.abstractResults of experiments on numerical data sets discretized using two methods—global versions of Equal Frequency per Interval and Equal Interval Width-are presented. Globalization of both methods is based on entropy. For discretized data sets left and right reducts were computed. For each discretized data set and two data sets, based, respectively, on left and right reducts, we applied ten-fold cross validation using the C4.5 decision tree generation system. Our main objective was to compare the quality of all three types of data sets in terms of an error rate. Additionally, we compared complexity of generated decision trees. We show that reduction of data sets may only increase the error rate and that the decision trees generated from reduced decision sets are not simpler than the decision trees generated from non-reduced data sets.en_US
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.subjectData miningen_US
dc.subjectNumerical attributesen_US
dc.titleReduced Data Sets and Entropy-Based Discretizationen_US
kusw.kuauthorGrzymala-Busse, Jerzy W.
kusw.kudepartmentElectrical Engineering and Computer Scienceen_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Except where otherwise noted, this item's license is described as: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.