An Alternative to Chaid Segmentation Algorithm Based on Entropy.

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? n...

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Main Authors: Galindo Villard?n, Mar?a Purificaci?n, Vicente Villard?n, Jos? Luis, Dorado D?az, Ana, Vicente Galindo, Mar?a Purificaci?n, Patino Alonso, Mar?a Carmen
Format: Artículo
Language: Español
Published: 2015
Online Access: http://revistas.ucr.ac.cr/index.php/matematica/article/view/2127
http://hdl.handle.net/10669/12978
Summary: 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.