Mining optimal decision trees from itemset lattices
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Simplify Multi-valued Decision Trees
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Analysis of time series data with predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Integrating decision tree learning into inductive databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Beam search induction and similarity constraints for predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Optimal constraint-based decision tree induction from itemset lattices
Data Mining and Knowledge Discovery
Constraint based induction of multi-objective regression trees
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Classification is an important problem in data mining. Given a database of records, each with a class label, a classifier generates a concise and meaningful description for each class that can be used to classify subsequent records. A number of popular classifiers construct decision trees to generate class models. Frequently, however, the constructed trees are complex with hundreds of nodes and thus difficult to comprehend, a fact that calls into question an often-cited benefit that decision trees are easy to interpret. In this paper, we address the problem of constructing “simple” decision trees with few nodes that are easy for humans to interpret. By permitting users to specify constraints on tree size or accuracy, and then building the “best” tree that satisfies the constraints, we ensure that the final tree is both easy to understand and has good accuracy. We develop novel branch-and-bound algorithms for pushing the constraints into the building phase of classifiers, and pruning early tree nodes that cannot possibly satisfy the constraints. Our experimental results with real-life and synthetic data sets demonstrate that significant performance speedups and reductions in the number of nodes expanded can be achieved as a result of incorporating knowledge of the constraints into the building step as opposed to applying the constraints after the entire tree is built.