Designing Storage Efficient Decision Trees
IEEE Transactions on Computers
The multi-player version of minimax displays game-tree pathology
Artificial Intelligence
Improving greedy algorithms by lookahead-search
Journal of Algorithms
Decision Quality As a Function of Search Depth on Game Trees
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
Classification and regression: money *can* grow on trees
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple Comparisons in Induction Algorithms
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Texture Based Look-Ahead for Decision-Tree Induction
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
The Biases of Decision Tree Pruning Strategies
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
Knowledge evaluation: statistical evaluations
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Simplifying decision trees: A survey
The Knowledge Engineering Review
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Generalized skewing for functions with continuous and nominal attributes
ICML '05 Proceedings of the 22nd international conference on Machine learning
A simple regression based heuristic for learning model trees
Intelligent Data Analysis
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
Moving towards efficient decision tree construction
Information Sciences: an International Journal
Connectionist theory refinement: genetically searching the space of network topologies
Journal of Artificial Intelligence Research
Behavioral-fusion control based on reinforcement learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Ensemble missing data techniques for software effort prediction
Intelligent Data Analysis
Pareto-optimality of oblique decision trees from evolutionary algorithms
Journal of Global Optimization
Induced states in a decision tree constructed by Q-learning
Information Sciences: an International Journal
Parallel data mining revisited. better, not faster
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Information Sciences: an International Journal
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The standard approach to decision tree induction is a top-down greedy agorithm that makes locally optimal irrevocable decisions at each node of a tree. In this paper we empircally study an alternative approach in which the algorithms use one-level lookahead to decide what test to use at a node. we systematically compare using a very large number of artificial data sets the quality of dimension trees induced by the greedy approach to that of trees induced using lookahead. The main observations from our experiments are (1) the greedy approach consistently produced trees that were just as at accurate as trees produced with the much more expensive lookahead step and (n) we observed many instances of pathology, i.e, lookahead produced trees that were both larger and less accurate than trees produced without it.