Abstract
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.
Highlights
IntroductionThe problem of reducing (or selecting) the set of attributes (or features) is known for many decades [1,2,3,4,5,6]
The problem of reducing the set of attributes is known for many decades [1,2,3,4,5,6]
Our results show again that reduction of data sets causes increase of an error rate
Summary
The problem of reducing (or selecting) the set of attributes (or features) is known for many decades [1,2,3,4,5,6]. It is a central topic in multivariate statistics and data analysis [7]. It was recognized as a variant of the Set Covering problem in [3]. An analysis of feature selection methods, presented in [7], included filter, wrapper and embedded methods, based on mathematical optimization, e.g., on linear programming. An algorithm for discretization that removes redundant attributes was presented in [8,9,10].
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