Pruning Algorithms for Rule Learning
Machine Learning
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
Parsimonious Least Norm Approximation
Computational Optimization and Applications
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Multiple Comparisons in Induction Algorithms
Machine Learning
ACM SIGKDD Explorations Newsletter
DCG induction using MDL and parsed corpora
Learning language in logic
Understanding the Crucial Role of AttributeInteraction in Data Mining
Artificial Intelligence Review
Mathematical Programming in Data Mining
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Metric-Based Methods for Adaptive Model Selection and Regularization
Machine Learning
Preventing Overfitting in Learning Text Patterns for Document Categorization
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Worst-Case Analysis of Rule Discovery
DS '01 Proceedings of the 4th International Conference on Discovery Science
The Biases of Decision Tree Pruning Strategies
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Knowledge evaluation: statistical evaluations
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Shallow parsing using noisy and non-stationary training material
The Journal of Machine Learning Research
Simplifying decision trees: A survey
The Knowledge Engineering Review
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
Applied Soft Computing
Non-strict heterogeneous Stacking
Pattern Recognition Letters
The class imbalance problem: A systematic study
Intelligent Data Analysis
The lack of a priori distinctions between learning algorithms
Neural Computation
Journal of Artificial Intelligence Research
Avoiding overfitting with BP-SOM
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
A system for induction of oblique decision trees
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
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Bernoulli's principle of insufficient reason and conservation of information in computer search
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Evolutionary synthesis of nand logic: dissecting a digital organism
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Evaluating learning algorithms and classifiers
International Journal of Intelligent Information and Database Systems
Sparse data and the effect of overfitting avoidance in decision tree induction
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A new metric-based approach to model selection
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Complex feature alternating decision tree
International Journal of Intelligent Systems Technologies and Applications
Computational properties of probabilistic neural-networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Gene selection and classification using Taguchi chaotic binary particle swarm optimization
Expert Systems with Applications: An International Journal
Discrete decision tree induction to avoid overfitting on categorical data
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Assessing the quality and cleaning of a software project dataset: an experience report
EASE'06 Proceedings of the 10th international conference on Evaluation and Assessment in Software Engineering
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Strategies for increasing predictive accuracy through selective pruning have been widely adopted by researchers in decision tree induction. It is easy to get the impression from research reports that there are statistical reasons for believing that these overfitting avoidance strategies do increase accuracy and that, as a research community, we are making progress toward developing powerful, general methods for guarding against overfitting in inducing decision trees. In fact, any overfitting avoidance strategy amounts to a form of bias and, as such, may degrade performance instead of improving it. If pruning methods have often proven successful in empirical tests, this is due, not to the methods, but to the choice of test problems. As examples in this article illustrate, overfitting avoidance strategies are not better or worse, but only more or less appropriate to specific application domains. We are not—and cannot be—making progress toward methods both powerful and general.