Information Processing Letters
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Designing Storage Efficient Decision Trees
IEEE Transactions on Computers
Computation and action under bounded resources
Computation and action under bounded resources
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Optimal composition of real-time systems
Artificial Intelligence
Machine Learning
An anytime approach to connectionist theory refinement: refining the topologies of knowledge-based neural networks
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Feature Generation Using General Constructor Functions
Machine Learning
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Incremental Induction of Decision Trees
Machine Learning
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Sequential skewing: an improved skewing algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
MC-tree: Improving Bayesian anytime classification
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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Most existing decision tree inducers are very fast due to their greedy approach. In many real-life applications, however, we are willing to allocate more time to get better decision trees. Our recently introduced LSID3 contract anytime algorithm allows computation speed to be traded for better tree quality. As a contract algorithm, LSID3 must be allocated its resources a priori, which is not always possible. In this work, we present IIDT, a general framework for interruptible induction of decision trees that need not be allocated resources a priori. The core of our proposed framework is an iterative improvement algorithm that repeatedly selects a subtree whose reconstruction is expected to yield the highest marginal utility. The algorithm then rebuilds the subtree with a higher allocation of resources. IIDT can also be configured to receive training examples as they become available, and is thus appropriate for incremental learning tasks. Empirical evaluation with several hard concepts shows that IIDT exhibits good anytime behavior and significantly outperforms greedy inducers when more time is available. A comparison of IIDT to several modern decision tree learners showed it to be superior.