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Reduced Data Sets and Entropy-Based Discretization

1
Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
2
Department of Artificial Intelligence, University of Information Technology and Management, 35–225 Rzeszow, Poland
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Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1051; https://doi.org/10.3390/e21111051
Received: 13 September 2019 / Revised: 23 October 2019 / Accepted: 25 October 2019 / Published: 28 October 2019
(This article belongs to the Special Issue The Ubiquity of Entropy)
Results 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. View Full-Text
Keywords: data mining; numerical attributes; discretization; entropy data mining; numerical attributes; discretization; entropy
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Grzymala-Busse, J.W.; Hippe, Z.S.; Mroczek, T. Reduced Data Sets and Entropy-Based Discretization. Entropy 2019, 21, 1051.

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