Abstract
The study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28] showed that such algorithm may construct decision trees whose average depth is arbitrarily far from the minimum. Hyafil and Rivest in [35] proved NP-hardness of DT problem that is constructing a tree with the minimum average depth for a diagnostic problem over 2-valued information system and uniform probability distribution. Cox et al. in [22] showed that for a two-class problem over information system, even finding the root node attribute for an optimal tree is an NP-hard problem.
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© 2011 Springer-Verlag Berlin Heidelberg
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Chikalov, I. (2011). Algorithms for Decision Tree Construction. In: Average Time Complexity of Decision Trees. Intelligent Systems Reference Library, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22661-8_4
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DOI: https://doi.org/10.1007/978-3-642-22661-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22660-1
Online ISBN: 978-3-642-22661-8
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