Selection of multinomial logit models via association rules analysis

Abstract

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

Model Selection for Poisson Regression via Association Rules Analysis

“…Note that the number of rules selected at this stage is relatively small compared to the total number of possible interactions for the dataset. In this work, we set the number of potential rules at between 30 and 50 (the same number recommended in Changpetch and Lin (2013a) and Changpetch and Lin (2013b)). The higher the number of variables, the higher the number of potential rules we select.…”

“…To our knowledge, there are no studies linking association rules analysis and classification rules mining to Poisson regression modeling. The methods proposed by Changpetch and Lin (2013a) and Changpetch and Lin (2013b) are limited to binary response variable and multinomial response cases. Poisson regression model is very important and different from the logistic regression model and the multinomial logit model.…”

Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset

“…In our previous work, Changpetch and Lin [5], we used ASA, specifically classification rule mining (CRM), to search for potential rules, which are then converted into potential interactions (low-and high-order) to serve as candidate predictors for the multinomial logit model. We used ASA, because unlike the decision tree it allows for a global search through which more potential interactions can be located and thus considered.…”

“…We use ASA to find relationships between the response and the categorical variables in terms of rules. However, ASA works only with categorical variables, which means that quantitative predictors have limited use in this method [5,6]. It is possible, though, to overcome this limitation.…”

Attitudinal choice models with applications in human decision making

“…The popularity of the logit models can be gauged through their applications in diverse domains in the recent times. They are applied in severity analysis, [15][16][17] price optimization, 18 revenue optimization, 19 location planning, 20 choice analysis problems, [21][22][23][24][25] risk analysis, [26][27][28][29][30][31] demand analysis, 32,33 data analytics, [34][35][36] regression analysis, [37][38][39] causal inference in medicine, 40 and forecasting, 41 to name a few.…”