Premium
Selection of multinomial logit models via association rules analysis
Author(s) -
Changpetch Pannapa,
Lin Dennis K.J.
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1242
Subject(s) - multinomial logistic regression , categorical variable , multinomial distribution , selection (genetic algorithm) , computer science , econometrics , logistic regression , feature selection , mixed logit , model selection , multinomial probit , logit , discrete choice , machine learning , mathematics
In this research, we propose a novel approach for a multinomial logit model selection procedure: specifically, we apply association rules analysis to identifying potential interactions for multinomial logit modeling. Interaction effects are very common in reality, but conventional multinomial logit model selection methods typically ignore them. This is especially true for higher‐order interactions. Here, we develop a model selection framework to address this problem. Specifically, we focus on building an optimal multinomial logit model by (1) exploring the combinations of input variables that have a significant impact on response (via association rules analysis); (2) selecting potential (low‐order and high‐order) interactions; (3) converting these potential interactions into new dummy variables; and (4) performing variable selections among all the input variables and the newly created dummy variables (interactions). Our model selection procedure is the first approach to provide a global search for potential interactions and establish the optimal combination of main effects and interaction effects in the multinomial logit model. In our investigation, we consider both simulated and real‐life datasets, thereby confirming the effectiveness and efficiency of this method. WIREs Comput Stat 2013, 5:68–77. doi: 10.1002/wics.1242 This article is categorized under: Data: Types and Structure > Categorical Data Statistical Models > Generalized Linear Models Statistical Models > Model Selection