@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.},
  address = {New York, NY, USA},
  author = {Kang, Dae-Ki and Sohn, Kiwook},
  doi = {http://dx.doi.org/10.1016/j.patcog.2008.07.009},
  interhash = {8c92a355c3401fac2c44b787ef8dd2ec},
  intrahash = {2238a6ae8a6d97a8835803d1bbcbb0d9},
  issn = {0031-3203},
  journal = {Pattern Recogn.},
  number = 1,
  pages = {84--92},
  publisher = {Elsevier Science Inc.},
  title = {Learning decision trees with taxonomy of propositionalized attributes},
  url = {http://portal.acm.org/citation.cfm?id=1413020},
  volume = 42,
  year = 2009
}