Modeling of discrete questionnaire data with dimension reduction
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Published:2022
Issue:1
Volume:32
Page:15-41
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ISSN:2336-4335
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Container-title:Neural Network World
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language:
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Short-container-title:NNW
Author:
Jozová Šárka,Uglickich Evženie,Nagy Ivan,Likhonina Raissa
Abstract
The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.
Publisher
Czech Technical University in Prague - Central Library
Subject
Artificial Intelligence,Hardware and Architecture,General Neuroscience,Software
Cited by
2 articles.
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