ART: A Hybrid Classification Model

Fernando Berzal; Juan Carlos Cubero; Daniel Sánchez; José María Serrano

doi:10.1023/B:MACH.0000008085.22487.a6

ART: A Hybrid Classification Model

Fernando Berzal, Juan Carlos Cubero, Daniel Sánchez, José María Serrano

Research output: Indexed journal article › Article › peer-review

40 Citations (Scopus)

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.

Original language	English
Pages (from-to)	67-92
Number of pages	26
Journal	Machine Learning
Volume	54
Issue number	1
DOIs	https://doi.org/10.1023/B:MACH.0000008085.22487.a6
Publication status	Published - Jan 2004
Externally published	Yes

Keywords

Association rules
Classification
Data Mining
Decision lists
Decision trees
Supervised learning

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10.1023/B:MACH.0000008085.22487.a6

Cite this

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AU - Sánchez, Daniel

AU - María Serrano, José

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KW - Classification

KW - Data Mining

KW - Decision lists

KW - Decision trees

KW - Supervised learning

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ART: A Hybrid Classification Model

Abstract

Keywords

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