An SE-tree-based induction and classification tool. Set Enumeration (SE) trees provide the basis for an induction and classification framework which generalizes decision trees. In this framework, called SE-Learn, rather than splitting according to a single attribute, one recursively branches on all (or most) relevant attributes. A single SE-tree economically embeds many decision trees, supporting a more expressive representation. SE-Learn benefits from many techniques developed for decision trees, e.g., attribute-selection and pruning measures. In particular, SE-Learn can be tailored to start off with anyone's favorite decision tree, and then improve upon it via further exploring the SE-tree. This hill-climbing algorithm allows trading time/space for added accuracy. Current studies show that SE-trees are particularly advantageous in domains where (relatively) few examples are available for training, and in noisy domains. Finally, SE-trees provide a unified framework for combining induced knowledge with knowledge available from other sources.
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