An Exact Probability Metric for Decision Tree Splitting and Stopping
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General and Efficient Multisplitting of Numerical Attributes
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A Machine Learning Approach to POS Tagging
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Bottom-Up Induction of Feature Terms
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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
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Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
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Discretization: An Enabling Technique
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On the quest for easy-to-understand splitting rules
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Using a mixture of probabilistic decision trees for direct prediction of protein function
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An inductive learning method for medical diagnosis
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Investigation and Reduction of Discretization Variance in Decision Tree Induction
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Boosting Applied toe Word Sense Disambiguation
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Lazy Induction of Descriptions for Relational Case-Based Learning
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Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set
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GD: A Measure Based on Information Theory for Attribute Selection
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Classification with Belief Decision Trees
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Ensemble Case-Based Reasoning: Collaboration Policies for Multiagent Cooperative CBR
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Components for Case-Based Reasoning Systems
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Annals of Mathematics and Artificial Intelligence
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A flexible POS tagger using an automatically acquired language model
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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A greedy algorithm for supervised discretization
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The Explanatory Power of Symbolic Similarity in Case-Based Reasoning
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Applying machine learning to Chinese temporal relation resolution
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On the Computational Complexity of Optimal Multisplitting
Fundamenta Informaticae - Intelligent Systems
Ranking Categorical Features Using Generalization Properties
The Journal of Machine Learning Research
Discovering Plausible Explanations of Carcinogenecity in Chemical Compounds
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Using explanations for determining carcinogenecity in chemical compounds
Engineering Applications of Artificial Intelligence
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Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data
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Iterative optimization and simplification of hierarchical clusterings
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Expert Systems with Applications: An International Journal
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Prediction by categorical features: generalization properties and application to feature ranking
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Automatic design of decision-tree algorithms with evolutionary algorithms
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Intelligent Data Analysis
Feature ranking fusion for text classifier
Intelligent Data Analysis
Evolutionary approach for automated component-based decision tree algorithm design
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
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This note introduces a new attribute selection measure for ID3-like inductive algorithms. This measure is based on a distance between partitions such that the selected attribute in a node induces the partition which is closest to the correct partition of the subset of training examples corresponding to this node. The relationship of this measure with Quinlan's information gain is also established. It is also formally proved that our distance is not biased towards attributes with large numbers of values. Experimental studies with this distance confirm previously reported results showing that the predictive accuracy of induced decision trees is not sensitive to the goodness of the attribute selection measure. However, this distance produces smaller trees than the gain ratio measure of Quinlan, especially in the case of data whose attributes have significantly different numbers of values.