Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Clustering categorical data: an approach based on dynamical systems
The VLDB Journal — The International Journal on Very Large Data Bases
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Proceedings of the 15th international conference on World Wide Web
Multivariate information bottleneck
Neural Computation
Canonicalization of database records using adaptive similarity measures
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multinomial event model based abstraction for sequence and text classification
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Some inequalities for information divergence and related measures of discrimination
IEEE Transactions on Information Theory
On the convexity of some divergence measures based on entropy functions
IEEE Transactions on Information Theory
Active rule learning using decision tree for resource management in Grid computing
Future Generation Computer Systems
Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers
Expert Systems with Applications: An International Journal
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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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.