Affiliations: Department of Computation Science and Artificial Intelligence, University of Granada, Granada, Spain
Correspondence:
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Corresponding author: Joaquín Abellán, Department of Computation Science and Artificial Intelligence, University of Granada, Granada 18071, Spain. E-mail: jabellan@decsai.ugr.es.
Abstract: In the analysis of data, the discovery of dependence relations can play a very important role. Our principal aim in this paper is to present a new score to determine when two categorical variables are independent. It can be resumed as an interval-valued score that is based on the Heckerman, Geiger, and Chickering's score, which can be used in supervised classification task. We carry out an empirical comparison with different scores to determine when two binary variables are independent. Also, we have considered the following measures: the Bayesian score metric, the Bayesian information criterion (BIC), the p-value of the Chi-square test for independence and the upper entropy score based on imprecise probabilities. We will see that our new score has a behavior that it is more similar to statistical tests from small samples and to Bayesian procedures for large samples. We find this behavior very appropriate for some types of problems.
