TY  - JOUR
AU  - Kang, Dae-Ki
AU  - Sohn, Kiwook
T1  - Learning decision trees with taxonomy of propositionalized attributes
JO  - Pattern Recogn.
PY  - 2009/
VL  - 42
IS  - 1
SP  - 84
EP  - 92
UR  - http://portal.acm.org/citation.cfm?id=1413020
M3  - http://dx.doi.org/10.1016/j.patcog.2008.07.009
KW  - background
KW  - decision
KW  - knowledge
KW  - ontology
KW  - taxonomy
KW  - tree
L1  - 
SN  - 
N1  - Learning decision trees with taxonomy of propositionalized attributes
N1  - 
AB  - 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.
ER  -

TY  - CONF
AU  - Freund, Yoav
AU  - Mason, Llew
A2  - 
T1  - The Alternating Decision Tree Learning Algorithm.
T2  - ICML
PB  - 
CY  - 
PY  - 1999/
M2  - 
VL  - 
IS  - 
SP  - 124
EP  - 133
UR  - http://www.lsmason.com/papers/ICML99-AlternatingTrees.pdf
M3  - 
KW  - weka
KW  - decision
KW  - adtree
KW  - tree
L1  - 
SN  - 
N1  - dblp
N1  - 
AB  - 
ER  -

TY  - CONF
AU  - Blockeel, Hendrik
AU  - Raedt, Luc De
AU  - Ramon, Jan
A2  - Shavlik, Jude W.
T1  - Top-Down Induction of Clustering Trees.
T2  - ICML
PB  - Morgan Kaufmann
CY  - 
PY  - 1998/
M2  - 
VL  - 
IS  - 
SP  - 55
EP  - 63
UR  - http://dblp.uni-trier.de/db/conf/icml/icml1998.html#BlockeelRR98
M3  - 
KW  - clustering
KW  - toread
KW  - tree
L1  - 
SN  - 1-55860-556-8
N1  - dblp
N1  - 
AB  - 
ER  -

TY  - BOOK
AU  - Quinlan, J. R.
A2  - 
T1  - C4.5 Programs for Machine Learning
PB  - Morgan Kaufmann, California
AD  - 
PY  - 1993/
VL  - 
IS  - 
SP  - 
EP  - 
UR  - 
M3  - 
KW  - learning
KW  - decision
KW  - entropy
KW  - *****
KW  - tree
KW  - ml
L1  - 
SN  - 1558602380
N1  - 
N1  - 
AB  - 
ER  -

TY  - JOUR
AU  - Safavian, S. R.
AU  - Landgrebe, D.
T1  - A survey of decision tree classifier methodology
JO  - Systems, Man and Cybernetics, IEEE Transactions on
PY  - 1991/
VL  - 21
IS  - 3
SP  - 660
EP  - 674
UR  - http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=97458
M3  - 
KW  - decision
KW  - survey
KW  - tree
L1  - 
SN  - 
N1  - CiteULike: A survey of decision tree classifier methodology
N1  - 
AB  - 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
ER  -