ML Anthology

ART: A Hybrid Classification Model

Fernando Berzal Galiano, Juan C. Cubero, Daniel Sánchez, José-María Serrano
MLJ 2004 pp. 67-92
doi:10.1023/B:MACH.0000008085.22487.A6 /mlj/2004/galiano2004mlj-art/

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.

PDF MLJ Semantic Scholar

Cite

Text

Galiano et al. "ART: A Hybrid Classification Model." Machine Learning, 2004. doi:10.1023/B:MACH.0000008085.22487.A6

Markdown

[Galiano et al. "ART: A Hybrid Classification Model." Machine Learning, 2004.](https://mlanthology.org/mlj/2004/galiano2004mlj-art/) doi:10.1023/B:MACH.0000008085.22487.A6

BibTeX

@article{galiano2004mlj-art,
  title     = {{ART: A Hybrid Classification Model}},
  author    = {Galiano, Fernando Berzal and Cubero, Juan C. and Sánchez, Daniel and Serrano, José-María},
  journal   = {Machine Learning},
  year      = {2004},
  pages     = {67-92},
  doi       = {10.1023/B:MACH.0000008085.22487.A6},
  volume    = {54},
  url       = {https://mlanthology.org/mlj/2004/galiano2004mlj-art/}
}