@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
}

@article{survey91safavian,
  abstract = {A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed},
  author = {Safavian, S. R. and Landgrebe, D.},
  booktitle = {Systems, Man and Cybernetics, IEEE Transactions on},
  interhash = {d191b7a5dd9037f7e05357e9be3cf1c2},
  intrahash = {348c3ca0090e508133fffdf656b2432a},
  journal = {Systems, Man and Cybernetics, IEEE Transactions on},
  number = 3,
  pages = {660--674},
  title = {A survey of decision tree classifier methodology},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=97458},
  volume = 21,
  year = 1991
}

@inproceedings{conf/icml/BlockeelRR98,
  author = {Blockeel, Hendrik and Raedt, Luc De and Ramon, Jan},
  booktitle = {ICML},
  crossref = {conf/icml/1998},
  date = {2002-12-04},
  editor = {Shavlik, Jude W.},
  interhash = {85a600daf6e2e2a8843d51929f4921b2},
  intrahash = {9fc52926e77d93a1e095b6404b184f22},
  isbn = {1-55860-556-8},
  pages = {55-63},
  publisher = {Morgan Kaufmann},
  title = {Top-Down Induction of Clustering Trees.},
  url = {http://dblp.uni-trier.de/db/conf/icml/icml1998.html#BlockeelRR98},
  year = 1998
}

@book{Qui93,
  author = {Quinlan, J. R.},
  interhash = {1a265267f55efc59cd96ecb93a69b520},
  intrahash = {da2798a9bd21fd49a31dde24cb605b1a},
  isbn = {1558602380},
  publisher = {Morgan Kaufmann, California},
  title = {{C4.5 Programs for Machine Learning}},
  year = 1993
}

@inproceedings{conf/icml/FreundM99,
  author = {Freund, Yoav and Mason, Llew},
  booktitle = {ICML},
  interhash = {030d102d33daa5a86618cdc06cab6790},
  intrahash = {f13469252d81f44de33eb4a43af2498e},
  pages = {124-133},
  title = {The Alternating Decision Tree Learning Algorithm.},
  url = {http://www.lsmason.com/papers/ICML99-AlternatingTrees.pdf},
  year = 1999
}