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This article presents GuideR, a user-guided rule induction algorithm, whichovercomes the largest limitation of the existing methods-the lack of thepossibility to introduce user's preferences or domain knowledge to the rulelearning process. Automatic selection of attributes and attribute ranges oftenleads to the situation in which resulting rules do not contain interestinginformation. We propose an induction algorithm which takes into account user'srequirements. Our method uses the sequential covering approach and is suitablefor classification, regression, and survival analysis problems. Theeffectiveness of the algorithm in all these tasks has been verifiedexperimentally, confirming guided rule induction to be a powerful data analysistool.

GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings