Optimal Partitioning for Classification and Regression Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Ensemble missing data techniques for software effort prediction
Intelligent Data Analysis
Learning Classification Programs: The Genetic Algorithm Approach
Fundamenta Informaticae
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Techniques for discovering rules by induction from large collections of instances are developed. These are based on an iterative scheme for dividing the instances into two sets, only one of which needs to be randomly accessible. These techniques have made it possible to discover complex rules from data bases containing many thousands of instances. Results of several experiments using them are reported.