Summary
This paper aimed at two following objectives. One was the introduction of a new measure (R-measure) of dependency between groups of attributes in a data set, inspired by the notion of dependency of attribute in the rough set theory. The second was the application of this measure to the problem of attribute selection in decision tree induction, and an experimental comparative evaluation of decision tree systems using R-measure and other different attribute selection measures most of them are widely used in machine learning: gain-ratio, gini-index, d N distance, relevance, x 2.
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© 1998 Springer Japan
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Ho, T.B., Nguyen, T.D., Kimura, M. (1998). Induction of Decision Trees Based on the Rough Set Theory. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_22
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DOI: https://doi.org/10.1007/978-4-431-65950-1_22
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-70208-5
Online ISBN: 978-4-431-65950-1
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