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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
