%0 %0 Journal Article %A Kang, Dae-Ki & Sohn, Kiwook %D 2009 %T Learning decision trees with taxonomy of propositionalized attributes %E %B Pattern Recogn. %C %I Elsevier Science Inc. %V 42 %6 %N 1 %P 84--92 %& %Y %S %7 %8 %9 %? %! %Z %@ 0031-3203 %( %) %* %L %M %1 %2 Learning decision trees with taxonomy of propositionalized attributes %3 article %4 %# %$ %F 1413020 %K background, decision, knowledge, ontology, taxonomy, tree %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. %Z %U http://portal.acm.org/citation.cfm?id=1413020 %+ %^