Elements of information theory
Elements of information theory
Floating search methods in feature selection
Pattern Recognition Letters
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Physica D
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Knowledge and Data Engineering
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Agglomerative independent variable group analysis
Neurocomputing
Speeding Up Feature Subset Selection Through Mutual Information Relevance Filtering
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Advances in Feature Selection with Mutual Information
Similarity-Based Clustering
Posterior probability profiles for the automated assessment of the recovery of stroke patients
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Weighted mutual information for feature selection
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Hi-index | 0.00 |
A hybrid filter/wrapper feature subset selection algorithm for regression is proposed. First, features are filtered by means of a relevance and redundancy filter using mutual information between regression and target variables. We introduce permutation tests to find statistically significant relevant and redundant features. Second, a wrapper searches for good candidate feature subsets by taking the regression model into account. The advantage of a hybrid approach is threefold. First, the filter provides interesting features independently from the regression model and, hence, allows for an easier interpretation. Secondly, because the filter part is computationally less expensive, the global algorithm will faster provide good candidate subsets compared to a stand-alone wrapper approach. Finally, the wrapper takes the bias of the regression model into account, because the regression model guides the search for optimal features. Results are shown for the ‘Boston housing’ and ‘orange juice’ benchmarks based on the multilayer perceptron regression model.