An Alternative to Chaid Segmentation Algorithm Based on Entropy.

  1. Galindo Villardón, María Purificación 1
  2. Vicente Villardón, José Luis 1
  3. Dorado Díaz, Ana 1
  4. Vicente Galindo, María Purificación 1
  5. Patino Alonso, María Carmen 1
  1. 1 Universidad de Salamanca, Departamento de Estadística
Revue:
Revista de Matemática: Teoría y Aplicaciones

ISSN: 2215-3373,  2215-3373

Année de publication: 2010

Volumen: 17

Número: 2

Pages: 179-197

Type: Article

DOI: 10.15517/RMTA.V17I2.2127 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

D'autres publications dans: Revista de Matemática: Teoría y Aplicaciones

Résumé

The CHAID (Chi-Squared Automatic Interaction Detection) treebased segmentation technique has been found to be an effective approach for obtaining meaningful segments that are predictive of a K-category (nominal or ordinal) criterion variable. CHAID was designed to detect, in an automatic way, the  nteraction between several categorical or ordinal predictors in explaining a categorical response, but, this may not be true when Simpson’s paradox is present. This is due to the fact that CHAID is a forward selection algorithm based on the marginal counts. In this paper we propose a backwards elimination algorithm that starts with the full set of predictors (or full tree) and eliminates predictors progressively. The elimination procedure is based on Conditional Independence contrasts using the concept of entropy. The proposed procedure is compared to CHAID.

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