Tree classifier design with a permutation statistic
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Learning decision rules in noisy domains
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International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Computational limitations on learning from examples
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Extensions to the CART algorithm
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Inferring decision trees using the minimum description length principle
Information and Computation
Boolean Feature Discovery in Empirical Learning
Machine Learning
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An incremental method for finding multivariate splits for decision trees
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Improved training via incremental learning
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Improved Estimates for the Accuracy of Small Disjuncts
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An Iterative Growing and Pruning Algorithm for Classification Tree Design
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A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction
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A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The application of confidence interval error analysis to the design of decision tree classifiers
Pattern Recognition Letters
Machine Learning
Machine Learning
Bottom-up induction of oblivious read-once decision graphs
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
Bottom-up induction of oblivious read-once decision graphs: strengths and limitations
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Decision tree pruning: biased or optimal?
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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Recursive Automatic Bias Selection for Classifier Construction
Machine Learning - Special issue on bias evaluation and selection
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
IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
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Advances in knowledge discovery and data mining
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Knowledge Acquisition Via Incremental Conceptual Clustering
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The Replication Problem: A Constructive Induction Approach
EWSL '91 Proceedings of the European Working Session on Machine Learning
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
On Estimating Probabilities in Tree Pruning
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Decision Tree Pruning as a Search in the State Space
ECML '93 Proceedings of the European Conference on Machine Learning
Pruning Multivariate Decision Trees by Hyperplane Merging
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Simplifying Decision Trees by Pruning and Grafting: New Results (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Deconstructing the Digit Recognition Problem
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
A Practical Approach to Feature Selection
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INRECA: A Seamlessly Integrated System Based on Inductive Inference and Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Linear Machine Decision Trees
A Kolmogorov-Smirnoff Metric for Decision Tree Induction
A Kolmogorov-Smirnoff Metric for Decision Tree Induction
Perceptrons: An Introduction to Computational Geometry
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Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Constructing nominal X-of-N attributes
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Oblivious decision trees graphs and top down pruning
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ACM SIGKDD Explorations Newsletter
Conversational Case-Based Reasoning
Applied Intelligence
A Generalised Approach to Similarity-Based Retrieval in Recommender Systems
Artificial Intelligence Review
Mining Comprehensible Rules from Data with an Ant Colony Algorithm
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Sampling Strategies for Targeting Rare Groups from a Bank Customer Database
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Sequential Diagnosis in the Independence Bayesian Framework
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Boosted Tree Ensembles for Solving Multiclass Problems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
An Empirical Comparison of Pruning Methods for Ensemble Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Industry: the use of modern heuristic algorithms for mining insurance data
Handbook of data mining and knowledge discovery
Design and evaluation of visualization support to facilitate decision trees classification
International Journal of Human-Computer Studies
Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
IEEE Transactions on Knowledge and Data Engineering
Hybrid systems of local basis functions
Intelligent Data Analysis
Reducing decision tree fragmentation through attribute value grouping: A comparative study
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Artificial Intelligence Review
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WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
A bottom-up approach to discover transition rules of cellular automata using ant intelligence
International Journal of Geographical Information Science
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INFORMS Journal on Computing
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Distributed and Parallel Databases
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SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
The inverse classification problem
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ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Learning search heuristics for finding objects in structured environments
Robotics and Autonomous Systems
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Mining staff assignment rules from event-based data
BPM'05 Proceedings of the Third international conference on Business Process Management
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
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RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
Decision tree selection in an industrial machine fault diagnostics
MEDI'12 Proceedings of the 2nd international conference on Model and Data Engineering
FPTASs for trimming weighted trees
Theoretical Computer Science
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Evolutionary Computation
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Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy, and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems.