The complexity of Boolean functions
The complexity of Boolean functions
Introduction to algorithms
On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
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
Efficient agnostic PAC-learning with simple hypothesis
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
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)
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Class-Driven Statistical Discretization of Continuous Attributes (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Searching for Features Defined by Hyperplanes
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
OC1: randomized induction of oblique decision trees
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Expert Systems with Applications: An International Journal
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We present an optimal hyperplane searching method for decision tables using Genetic Algorithms. This method can be used to construct a decision tree for a given decision table. We also present some properties of the set of hyperplanes determined by our methods and evaluate an upper bound on the depth of the constructed decision tree.