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Based on the used feature construction strategy, this leads to a table of very high dimensionality with a lot of irrelevant and\/or redundant features that can negatively affect the predictive performance. In this paper, we propose a modification of the traditional two-step framework to overcome such problems. The proposed approach evaluates the features during the construction phase and reports only a subset of highly predictive features to the propositional learner. We present an implementation of this approach using a genetic algorithm to search for an optimal feature subset. Our experiments on a number of benchmark datasets suggest that the modified framework can help propositionalization methods to significantly improve their predictive performance.<\/jats:p>","DOI":"10.1142\/s0218194018400260","type":"journal-article","created":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T08:44:18Z","timestamp":1547541858000},"page":"1739-1754","source":"Crossref","is-referenced-by-count":1,"title":["Evolutionary Propositionalization of Multi-Relational Data \u2014 Research Notes"],"prefix":"10.1142","volume":"28","author":[{"given":"Valentin","family":"Kassarnig","sequence":"first","affiliation":[{"name":"Institute of Software Technology, Graz University of Technology, Inffeldgasse 16b\/II, 8010 Graz, Austria"}]},{"given":"Franz","family":"Wotawa","sequence":"additional","affiliation":[{"name":"Institute of Software Technology, Graz University of Technology, Inffeldgasse 16b\/II, 8010 Graz, Austria"}]}],"member":"219","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"S0218194018400260BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/S0957-4174(00)00027-0"},{"key":"S0218194018400260BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.06.024"},{"key":"S0218194018400260BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.omega.2004.07.024"},{"key":"S0218194018400260BIB004","doi-asserted-by":"publisher","DOI":"10.1145\/545151.545178"},{"key":"S0218194018400260BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/5254.809570"},{"key":"S0218194018400260BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2010.08.008"},{"key":"S0218194018400260BIB011","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-015-0065-x"},{"key":"S0218194018400260BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2006.05.128"},{"key":"S0218194018400260BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.06.009"},{"issue":"4","key":"S0218194018400260BIB015","first-page":"275","volume":"13","author":"Kramer S.","year":"2000","journal-title":"AI Comm."},{"key":"S0218194018400260BIB016","series-title":"Ch. 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