Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An estimator of the mutual information based on a criterion for independence
Computational Statistics & Data Analysis
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Application for Electroencephalogram Mining for Epileptic Seizure Prediction
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Epileptic Seizure Classification Using Neural Networks with 14 Features
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Estimation of Mutual Information: A Survey
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Selecting small audio feature sets in music classification by means of asymmetric mutation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Estimating redundancy information of selected features in multi-dimensional pattern classification
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Time series feature evaluation in discriminating preictal EEG states
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Time-efficient estimation of conditional mutual information for variable selection in classification
Computational Statistics & Data Analysis
| Hi-index | 12.05 |
Mutual information (MI) is used in feature selection to evaluate two key-properties of optimal features, the relevance of a feature to the class variable and the redundancy of similar features. Conditional mutual information (CMI), i.e., MI of the candidate feature to the class variable conditioning on the features already selected, is a natural extension of MI but not so far applied due to estimation complications for high dimensional distributions. We propose the nearest neighbor estimate of CMI, appropriate for high-dimensional variables, and build an iterative scheme for sequential feature selection with a termination criterion, called CMINN. We show that CMINN is equivalent to feature selection MI filters, such as mRMR and MaxiMin, in the presence of solely single feature effects, and more appropriate for combined feature effects. We compare CMINN to mRMR and MaxiMin on simulated datasets involving combined effects and confirm the superiority of CMINN in selecting the correct features (indicated also by the termination criterion) and giving best classification accuracy. The application to ten benchmark databases shows that CMINN obtains the same or higher classification accuracy compared to mRMR and MaxiMin at a smaller cardinality of the selected feature subset.