| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Kang, D.-K. & Sohn, K. | Learning decision trees with taxonomy of propositionalized attributes | 2009 | Pattern Recogn. | article | DOIURL |
| 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. | |||||
BibTeX:
@article{1413020,
author = {Kang, Dae-Ki and Sohn, Kiwook},
title = {Learning decision trees with taxonomy of propositionalized attributes},
journal = {Pattern Recogn.},
publisher = {Elsevier Science Inc.},
year = {2009},
volume = {42},
number = {1},
pages = {84--92},
url = {http://portal.acm.org/citation.cfm?id=1413020},
doi = {http://dx.doi.org/10.1016/j.patcog.2008.07.009}
}
|
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