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Randomized Separate and Conquer Rule induction
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2017 (English)In: http://iccda.org/index.html, ACM Press , 2017Conference paper (Refereed)
Abstract [en]

Rule learning comes in many forms, here we investigate a modified version of Separate and Conquer (SAC) learning to see if it improves the predictive performance of the induced predictive models. Our modified version of SAC has a hyperparameter which is used to specify the amount of examples that should not be removed from the induction. This selection is done at random and as a consequence the SAC algorithm will produce more and diverse rules, given the hyperparameter setting. The modified algorithm has been implemented in both an unordered single rule set setting as well as in an ensemble rule set setting. Both of these settings have been evaluated empirically on a number of datasets. The results show that in the single rule set setting the modified version significantly improves the predictive performance, at the cost of more rules, which was expected. In the ensemble setting the combined method of bagging and the modified SAC algorithm did not perform as good as expected, while using only the modified SAC algorithm in ensemble setting performed better than expected.

Place, publisher, year, edition, pages
ACM Press , 2017.
Keyword [en]
Rule learning, Theory of randomized search heuristics
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-149273DOI: 10.1145/3093241.3093284ISBN: 978-1-4503-5241-3 OAI: oai:DiVA.org:su-149273DiVA: diva2:1159989
Available from: 2017-11-24 Created: 2017-11-24

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