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 Accéder directement au contenu
Rapport (Rapport De Recherche) Année : 1997

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

Résumé

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 et versions

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

Identifiants

  • HAL Id : inria-00073377 , version 1

Citer

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⟩
118 Consultations
86 Téléchargements

Partager

Gmail Facebook X LinkedIn More