Increasing the Interpretability of Rules Induced from Imbalanced Data by Using Bayesian Confirmation Measures
[ 1 ] Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | [ P ] pracownik
2017
rozdział w monografii naukowej / referat
angielski
- Bayesian confirmation measures
- interpretability of rules
- class imbalance
- rule post-pruning
EN Approaches to support an interpretation of rules induced from imbalanced data are discussed. In this paper, the rule learning algorithm BRACID dedicated to class imbalance is considered. As it may induce too many rules, which hinders their interpretation, their filtering is applied. We introduce three different strategies, which aim at selecting rules having good descriptive characteristics. The strategies are based on combining Bayesian confirmation measures with rule support, which have not yet been studied in the class imbalance context. Experimental results show that these strategies reduce the number of rules and improve values of rule interestingness measures at the same time, without considerable losses of prediction abilities, especially for the minority class.
02.02.2017
84 - 98
20