Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Efficient Dimensionality Reduction Approaches for Feature Selection
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
A review of feature selection techniques in bioinformatics
Bioinformatics
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Relevance and Redundancy Analysis for Ensemble Classifiers
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
On Feature Selection, Bias-Variance, and Bagging
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Stable and Accurate Feature Selection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
The Journal of Machine Learning Research
Combining feature subsets in feature selection
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Accuracy/Diversity and Ensemble MLP Classifier Design
IEEE Transactions on Neural Networks
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Small number of samples with high dimensional feature space leads to degradation of classifier performance for machine learning, statistics and data mining systems. This paper presents a bootstrap feature selection for ensemble classifiers to deal with this problem and compares with traditional feature selection for ensemble (select optimal features from whole dataset before bootstrap selected data). Four base classifiers: Multilayer Perceptron, Support Vector Machines, Naive Bayes and Decision Tree are used to evaluate the performance of UCI machine learning repository and causal discovery datasets. Bootstrap feature selection algorithm provides slightly better accuracy than traditional feature selection for ensemble classifiers.