Predicate selection for structural decision trees

K. S. Ng; J. W. Lloyd

doi:10.1007/11536314_16

Predicate selection for structural decision trees

K. S. Ng^*, J. W. Lloyd

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	264-278
Number of pages	15
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3625
DOIs	https://doi.org/10.1007/11536314_16
Publication status	Published - 2005
Event	15th International Conference on Inductive Logic Programming, ILP 2005 - Bonn, Germany Duration: 10 Aug 2005 → 13 Aug 2005

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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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year = "2005",

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AU - Lloyd, J. W.

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AB - 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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U2 - 10.1007/11536314_16

DO - 10.1007/11536314_16

M3 - Conference article

AN - SCOPUS:26944440088

SN - 0302-9743

VL - 3625

SP - 264

EP - 278

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 15th International Conference on Inductive Logic Programming, ILP 2005

Y2 - 10 August 2005 through 13 August 2005

ER -

Predicate selection for structural decision trees

Abstract

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