Abstract
We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in classification learning, where we reconsider the use of accuracy as a predicate selection function and show that, on practical grounds, it is a better alternative to other commonly used functions. The second is in regression learning, where we consider the standard mean squared error measure and give a predicate pruning result for it.
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Ng, K.S., Lloyd, J.W. (2005). Predicate Selection for Structural Decision Trees. In: Kramer, S., Pfahringer, B. (eds) Inductive Logic Programming. ILP 2005. Lecture Notes in Computer Science(), vol 3625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536314_16
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DOI: https://doi.org/10.1007/11536314_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28177-1
Online ISBN: 978-3-540-31851-4
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