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Pattern recognition: statistical, structural and neural approaches
C4.5: programs for machine learning
C4.5: programs for machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
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Wrappers for feature subset selection
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Expert Systems with Applications: An International Journal
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Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
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Data & Knowledge Engineering
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EURASIP Journal on Applied Signal Processing
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MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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Pattern Recognition Letters
Expert Systems with Applications: An International Journal
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Intelligent Decision Technologies
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HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Selective enhancement learning in competitive learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Real-time activity classification using ambient and wearable sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Emotion recognition from speech signals using new harmony features
Signal Processing
Feature subspace ensembles: a parallel classifier combination scheme using feature selection
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
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MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Combining functional networks and sensitivity analysis as wrapper method for feature selection
Expert Systems with Applications: An International Journal
Gene selection and classification using Taguchi chaotic binary particle swarm optimization
Expert Systems with Applications: An International Journal
Multi-objective feature selection in music genre and style recognition tasks
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Artificial Intelligence in Medicine
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Weighted mutual information for feature selection
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
Less biased measurement of feature selection benefits
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Environmental Modelling & Software
Supervised feature subset selection with ordinal optimization
Knowledge-Based Systems
A survey on feature selection methods
Computers and Electrical Engineering
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
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This paper addresses a common methodological flaw in the comparison of variable selection methods. A practical approach to guide the search or the selection process is to compute cross-validation performance estimates of the different variable subsets. Used with computationally intensive search algorithms, these estimates may overfit and yield biased predictions. Therefore, they cannot be used reliably to compare two selection methods, as is shown by the empirical results of this paper. Instead, like in other instances of the model selection problem, independent test sets should be used for determining the final performance. The claims made in the literature about the superiority of more exhaustive search algorithms over simpler ones are also revisited, and some of them infirmed.