A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection and Dualities in Maximum Entropy Discrimination
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Learning class-discriminative dynamic Bayesian networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Fast algorithms for robust classification with Bayesian nets
International Journal of Approximate Reasoning
A Stochastic Algorithm for Feature Selection in Pattern Recognition
The Journal of Machine Learning Research
Consistent Feature Selection for Pattern Recognition in Polynomial Time
The Journal of Machine Learning Research
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
International Journal of Hybrid Intelligent Systems - HIS 2007
Boosted Bayesian network classifiers
Machine Learning
Bayesian classifiers based on kernel density estimation: Flexible classifiers
International Journal of Approximate Reasoning
Feature selection with dynamic mutual information
Pattern Recognition
Bayesian network models for hierarchical text classification from a thesaurus
International Journal of Approximate Reasoning
Discriminative model selection for belief net structures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning locally minimax optimal Bayesian networks
International Journal of Approximate Reasoning
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Strong optimality of the normalized ML models as universal codes and information in data
IEEE Transactions on Information Theory
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Learning Bayesian network classifiers by risk minimization
International Journal of Approximate Reasoning
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When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features for such classifiers. To this end, we propose a new definition of the concept of redundancy in noisy data. For comparing alternative classifiers, we use the Minimum Description Length for Feature Selection (MDL-FS) function that we introduced before. Our function differs from the well-known MDL function in that it captures a classifier's conditional log-likelihood. We show that the MDL-FS function serves to identify redundancy at different levels and is able to eliminate redundant features from different types of classifier. We support our theoretical findings by comparing the feature-selection behaviours of the various functions in a practical setting. Our results indicate that the MDL-FS function is more suited to the task of feature selection than MDL as it often yields classifiers of equal or better performance with significantly fewer attributes.