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ILP
2005
Springer

Predicate Selection for Structural Decision Trees

9 years 11 months ago
Predicate Selection for Structural Decision Trees
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.
Kee Siong Ng, John W. Lloyd
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ILP
Authors Kee Siong Ng, John W. Lloyd
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