%0 Journal Article %1 1413020 %A Kang, Dae-Ki %A Sohn, Kiwook %C New York, NY, USA %D 2009 %I Elsevier Science Inc. %J Pattern Recogn. %K background decision knowledge ontology taxonomy tree %N 1 %P 84--92 %R http://dx.doi.org/10.1016/j.patcog.2008.07.009 %T Learning decision trees with taxonomy of propositionalized attributes %U http://portal.acm.org/citation.cfm?id=1413020 %V 42 %X We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce propositionalized attribute taxonomy guided decision tree learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce propositionalized attribute taxonomy learner (PAT-Learner) that automatically constructs taxonomy from data. PAT-DTL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the literals of decision rules from data and propositionalized attribute taxonomy. PAT-Learner propositionalizes attributes and hierarchically clusters the propositionalized attributes based on the distribution of class labels that co-occur with them to generate a taxonomy. Our experimental results on UCI repository data sets show that the proposed algorithms can generate a decision tree that is generally more compact than and is sometimes comparably accurate to those produced by standard decision tree learners.

@article{1413020, abstract = {We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce propositionalized attribute taxonomy guided decision tree learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce propositionalized attribute taxonomy learner (PAT-Learner) that automatically constructs taxonomy from data. PAT-DTL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the literals of decision rules from data and propositionalized attribute taxonomy. PAT-Learner propositionalizes attributes and hierarchically clusters the propositionalized attributes based on the distribution of class labels that co-occur with them to generate a taxonomy. Our experimental results on UCI repository data sets show that the proposed algorithms can generate a decision tree that is generally more compact than and is sometimes comparably accurate to those produced by standard decision tree learners.}, added-at = {2009-01-26T16:55:39.000+0100}, address = {New York, NY, USA}, author = {Kang, Dae-Ki and Sohn, Kiwook}, biburl = {https://www.bibsonomy.org/bibtex/22238a6ae8a6d97a8835803d1bbcbb0d9/hotho}, description = {Learning decision trees with taxonomy of propositionalized attributes}, doi = {http://dx.doi.org/10.1016/j.patcog.2008.07.009}, interhash = {8c92a355c3401fac2c44b787ef8dd2ec}, intrahash = {2238a6ae8a6d97a8835803d1bbcbb0d9}, issn = {0031-3203}, journal = {Pattern Recogn.}, keywords = {background decision knowledge ontology taxonomy tree}, number = 1, pages = {84--92}, publisher = {Elsevier Science Inc.}, timestamp = {2009-01-26T16:55:39.000+0100}, title = {Learning decision trees with taxonomy of propositionalized attributes}, url = {http://portal.acm.org/citation.cfm?id=1413020}, volume = 42, year = 2009 }