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
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Linear-Time Preprocessing in Optimal Numerical Range Partitioning
Journal of Intelligent Information Systems - Special issue: A survey of research questions for intelligent information systems in education
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
Data Mining and Knowledge Discovery
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
The Evaluation of Predictive Learners: Some Theoretical and Empirical Results
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
The Lumberjack Algorithm for Learning Linked Decision Forests
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
MML Inference of Decision Graphs with Multi-way Joins
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Information-Based Classification by Aggregating Emerging Patterns
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
Learning from Cluster Examples
Machine Learning
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Simplifying decision trees: A survey
The Knowledge Engineering Review
Models for machine learning and data mining in functional programming
Journal of Functional Programming
Using machine learning techniques to interpret WH-questions
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A programming paradigm for machine learning, with a case study of Bayesian networks
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Boosted decision graphs for NLP learning tasks
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Compression-Based Measures for Mining Interesting Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Extracting phoneme pronunciation information from corpora
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
On Feature Selection, Bias-Variance, and Bagging
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Journal of Artificial Intelligence Research
The lumberjack algorithm for learning linked decision forests
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Compact coding for hyperplane classifiers in heterogeneous environment
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
MML inference of oblique decision trees
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Learning hybrid bayesian networks by MML
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Summarizing data succinctly with the most informative itemsets
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
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Quinlan and Rivest have suggested a decision-tree inference method using the Minimum Description Length idea. We show that there is an error in their derivation of message lengths, which fortunately has no effect on the final inference. We further suggest two improvements to their coding techniques, one removing an inefficiency in the description of non-binary trees, and one improving the coding of leaves. We argue that these improvements are superior to similarly motivated proposals in the original paper.Empirical tests confirm the good results reported by Quinlan and Rivest, and show our coding proposals to lead to useful improvements in the performance of the method.