Reducing the Number of Binary Splits, in Decision Tree Induction, by means of an Hierarchical Classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 1997

Reducing the Number of Binary Splits, in Decision Tree Induction, by means of an Hierarchical Classification

Abstract

The main problem considered in this paper consists of binarizing categorical (nominal) attributes having a very large number of values (20^4 in our application). Few number of relevant binary attributes are gathered from each initial attribute. The significant idea consists in grouping the values of an attribute by means of an hierarchical classification method. The similarity between values is associated with the classes to be predicted. The solution that we propose is independant of the number of these classes and can be applied to various situations. A specific use of the obtained classification tree reduces very significantly the number of binary splits of the attribute value set that have to be retained. In fact and for complexity reasons, the hierarchical classification method is combined with formal decomposition and recomposition of the attribute value set. The ARCADE method that we have set up is mainly a powerful hybridation of the celebrated CART method, by our above outlined reduction method. The application of ARCADE, to the protein secondary structure prediction problem, proves the validity of our approach.
Fichier principal
Vignette du fichier
RR-3312.pdf (473.51 Ko) Télécharger le fichier

Dates and versions

inria-00073377 , version 1 (24-05-2006)

Identifiers

  • HAL Id : inria-00073377 , version 1

Cite

Israël-César Lerman, Joaquim F. Pinto da Costa. Reducing the Number of Binary Splits, in Decision Tree Induction, by means of an Hierarchical Classification. [Research Report] RR-3312, INRIA. 1997. ⟨inria-00073377⟩
116 View
80 Download

Share

Gmail Facebook Twitter LinkedIn More