Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Boolean Feature Discovery in Empirical Learning
Machine Learning
Elements of information theory
Elements of information theory
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Bottom-up induction of oblivious read-once decision graphs
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Branching on attribute values in decision tree generation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Bottom-up induction of oblivious read-once decision graphs: strengths and limitations
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
On the Practice of Branching Program Boosting
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Difficulty of Reduced Error Pruning of Leveled Branching Programs
Annals of Mathematics and Artificial Intelligence
Simplifying decision trees: A survey
The Knowledge Engineering Review
Tree structured classifiers, interconnected data, and predictive accuracy
Intelligent Data Analysis
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Expert Systems with Applications: An International Journal
Top-down induction of reduced ordered decision diagrams from neural networks
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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We describe a supervised learning algorithm, EODG that uses mutual information to build an oblivious decision tree. The tree is then converted to an Oblivious read-Once Decision Graph (OODG) by merging nodes at the same level of the tree. For domains that art appropriate for both decision trees and OODGs, performance is approximately the same as that of C4.5), but the number of nodes in the OODG is much smaller. The merging phase that converts the oblivious decision tree to an OODG provides a new way of dealing with the replication problem and a new pruning mechanism that works top down starting from the root. The pruning mechanism is well suited for finding symmetries and aids in recovering from splits on irrelevant features that may happen during the tree construction.