Optimal Partitioning for Classification and Regression Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Formation of clusters and resolution of ordinal attributes in ID3 classification trees
SAC '92 Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's
Learning k&mgr; decision trees on the uniform distribution
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
In-process improvement through defect data interpretation
IBM Systems Journal
On the boosting ability of top-down decision tree learning algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Post-process feedback with and without attribute focusing: a comparative evaluation
ICSE '93 Proceedings of the 15th international conference on Software Engineering
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Multiple Comparisons in Induction Algorithms
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A New Probabilistic Induction Method
Journal of Automated Reasoning
Modelling the Likelihood of Software Process Improvement: An Exploratory Study
Empirical Software Engineering
Learning Concepts by Arranging Appropriate Training Order
Minds and Machines
An Efficient Inductive Learning Method for Object-Oriented Database Using Attribute Entropy
IEEE Transactions on Knowledge and Data Engineering
Inductive Learning for Risk Classification
IEEE Expert: Intelligent Systems and Their Applications
A Case Study of Software Process Improvement During Development
IEEE Transactions on Software Engineering
Effect of pruning and early stopping on performance of a boosting ensemble
Computational Statistics & Data Analysis - Nonlinear methods and data mining
On the quest for easy-to-understand splitting rules
Data & Knowledge Engineering
Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning
IEEE Transactions on Knowledge and Data Engineering
Nonparametric Regularization of Decision Trees
ECML '00 Proceedings of the 11th European Conference on Machine Learning
A Unified Framework for Evaluation Metrics in Classification Using Decision Trees
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Possibilistic Induction in Decision-Tree Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Texture Based Look-Ahead for Decision-Tree Induction
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Feature Transformation and Multivariate Decision Tree Induction
DS '98 Proceedings of the First International Conference on Discovery Science
Construct robust rule sets for classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Domain-Specific Web Search with Keyword Spices
IEEE Transactions on Knowledge and Data Engineering
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
The Knowledge Engineering Review
Simplifying decision trees: A survey
The Knowledge Engineering Review
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
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Machine Learning
Special section: research in integrating learning capabilities into information systems
Journal of Management Information Systems - Special section: Research in integrating learning capabilities into information systems
Inductive learning for international financial analysis: a layered approach
Journal of Management Information Systems - Special section: Research in integrating learning capabilities into information systems
On the Computational Complexity of Optimal Multisplitting
Fundamenta Informaticae - Intelligent Systems
Neural network explanation using inversion
Neural Networks
Combining multiple class distribution modified subsamples in a single tree
Pattern Recognition Letters
Evaluating noise elimination techniques for software quality estimation
Intelligent Data Analysis
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Comparing probability measures using possibility theory: A notion of relative peakedness
International Journal of Approximate Reasoning
Rule quality for multiple-rule classifier: Empirical expertise and theoretical methodology
Intelligent Data Analysis
Rough set based approach for inducing decision trees
Knowledge-Based Systems
Using coverage as a model building constraint in learning classifier systems
Evolutionary Computation
Detection of e-mail concerning criminal activities using association rule-based decision tree
International Journal of Electronic Security and Digital Forensics
Parallel learning using decision trees: a novel approach
AMCOS'05 Proceedings of the 4th WSEAS International Conference on Applied Mathematics and Computer Science
Ranking Categorical Features Using Generalization Properties
The Journal of Machine Learning Research
Moving towards efficient decision tree construction
Information Sciences: an International Journal
A Bayesian Random Split to Build Ensembles of Classification Trees
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
An analysis of reduced error pruning
Journal of Artificial Intelligence Research
A scheme for feature construction and a comparison of empirical methods
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Optimisation of the decision tree technique applied to simulated sow herd datasets
Computers and Electronics in Agriculture
RBDT-1: A New Rule-Based Decision Tree Generation Technique
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
Prediction by categorical features: generalization properties and application to feature ranking
COLT'07 Proceedings of the 20th annual conference on Learning theory
Efficient multi-method rule learning for pattern classification machine learning and data mining
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Myths and legends in learning classification rules
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Inductive learning in a mixed paradigm setting
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
The attribute selection problem in decision tree generation
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
COGIN: symbolic induction with genetic algorithms
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Pattern discovery in distributed databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Separability of split value criterion with weighted separation gains
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
A VPRSM based approach for inducing decision trees
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
ComEnVprs: a novel approach for inducing decision tree classifiers
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Predicate selection for structural decision trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
An unsupervised feature selection framework based on clustering
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Extraction of diagnostic rules using recursive partitioning systems: A comparison of two approaches
Artificial Intelligence in Medicine
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Inducing decision trees with an ant colony optimization algorithm
Applied Soft Computing
On the Computational Complexity of Optimal Multisplitting
Fundamenta Informaticae - Intelligent Systems
On Learning Decision Structures
Fundamenta Informaticae
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
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One approach to induction is to develop a decision tree from a set of examples. When used with noisy rather than deterministic data, the method involves three main stages – creating a complete tree able to classify all the examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper is concerned with the first stage – tree creation – which relies on a measure for “goodness of split,” that is, how well the attributes discriminate between classes. Some problems encountered at this stage are missing data and multi-valued attributes. The paper considers a number of different measures and experimentally examines their behavior in four domains. The results show that the choice of measure affects the size of a tree but not its accuracy, which remains the same even when attributes are selected randomly.