Abstract
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.
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Notes
- 1.
\(c_3(H,E)=A(H,E)Z(H,E)\) in case of confirmation and
\(c_3(H,E)=-A(H,E)Z(H,E)\) in case of disconfirmation
where
\(Z(H,E)=1-P( \lnot H|E) \div P(\lnot H)\) in case of confirmation and
\(Z(H,E)=P(H|E) \div P(H)-1\) in case of disconfirmation;
\(A(H,E)=[P(E|H)-P(E)]\div [1-P(E)]\) in case of confirmation and
\(A(H,E)=[P(H)-P(H| \lnot E)] \div [1-P(H)]\) in case of disconfirmation.
- 2.
For simplicity we will further use a notation of a rule as R instead of (H, E) in symbols of measures.
- 3.
More detailed experimental results, including also the coverage option are provided at the page http://www.cs.put.poznan.pl/iszczech/publications/nfmcp-2016.html.
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The research was supported by NCN grant DEC-2013/11/B/ST6/00963.
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Napierała, K., Stefanowski, J., Szczȩch, I. (2017). Increasing the Interpretability of Rules Induced from Imbalanced Data by Using Bayesian Confirmation Measures. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_6
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