ART: A Hybrid Classification Model

Fernando Berzal; Juan Carlos Cubero; Daniel Sánchez; J. Mària Serrano

doi:10.1023/B:MACH.0000008085.22487.a6

ART: A Hybrid Classification Model

Fernando Berzal, Juan Carlos Cubero, Daniel Sánchez, J. Mària Serrano

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This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

Idioma original	Anglès
Pàgines (de-a)	67-92
Nombre de pàgines	26
Revista	Machine Learning
Volum	54
Número	1
DOIs	https://doi.org/10.1023/B:MACH.0000008085.22487.a6
Estat de la publicació	Publicada - de gen. 2004
Publicat externament	Sí

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title = "ART: A Hybrid Classification Model",

abstract = "This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized {"}Separate and Conquer{"} algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.",

keywords = "Association rules, Classification, Data Mining, Decision lists, Decision trees, Supervised learning",

author = "Fernando Berzal and Cubero, {Juan Carlos} and Daniel S{\'a}nchez and {M{\`a}ria Serrano}, J.",

year = "2004",

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doi = "10.1023/B:MACH.0000008085.22487.a6",

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AU - Cubero, Juan Carlos

AU - Sánchez, Daniel

AU - Mària Serrano, J.

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Y1 - 2004/1

N2 - This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

AB - This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

KW - Association rules

KW - Classification

KW - Data Mining

KW - Decision lists

KW - Decision trees

KW - Supervised learning

UR - http://www.scopus.com/inward/record.url?scp=0942277347&partnerID=8YFLogxK

U2 - 10.1023/B:MACH.0000008085.22487.a6

DO - 10.1023/B:MACH.0000008085.22487.a6

M3 - Article

AN - SCOPUS:0942277347

SN - 0885-6125

VL - 54

SP - 67

EP - 92

JO - Machine Learning

JF - Machine Learning

IS - 1

ER -

ART: A Hybrid Classification Model

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