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Activity Number: 677 - Variable Selection Methods in Statistical Learning
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #328952
Title: Variables and Interactions Generation for Logistic Regression Model via TreeNet and Association Rules
Author(s): Pannapa Changpetch*
Keywords: Association rules analysis; Logistic regression model; TreeNet; Interaction effect

This study establishes a new horizon for generating new variables and interactions for logistic regression using the two data mining techniques, TreeNet and association rules analysis. With TreeNet as the first step in our logistic model building, the new variables are generated by discretizing the quantitative variables. With association rules analysis as the second step, the new interactions are generated from all the original categorical variables and all the newly generated predictors from TreeNet. These newly generated variables and interactions (low- and high-order) are used as candidate predictors to build an optimal logistic regression model.

Authors who are presenting talks have a * after their name.

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