Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Using conjunction of attribute values for classification
Proceedings of the eleventh international conference on Information and knowledge management
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Generating neural networks through the induction of threshold logic unit trees
INBS '95 Proceedings of the First International Symposium on Intelligence in Neural and Biological Systems (INBS'95)
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Classification of wetland from TM imageries based on decision tree
WSEAS Transactions on Information Science and Applications
Classification of wetland from TM imageries based on decision tree
WSEAS Transactions on Information Science and Applications
Constructing nominal X-of-N attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper we present a new multivariate decision tree algorithm LMDT, which combines linear machines with decision trees. LMDT constructs each test in a linear machine and then eliminating irrelevant and noisy variables in a controlled manner. To examine LMDT''s ability to find good generalizations we present results for a variety of domains. We compare LMDT empirically to a univariate decision tree algorithm and observe that when multivariate tests are the appropriate bias for a given data set, LMDT finds small accurate trees.