Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection using Fuzzy Support Vector Machines
Fuzzy Optimization and Decision Making
Feature selection methods for text classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
A review of feature selection techniques in bioinformatics
Bioinformatics
A Feature Selection Approach for Network Intrusion Detection
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Classifier subset selection for biomedical named entity recognition
Applied Intelligence
Efficient entropy-based features selection for image retrieval
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SAINT '10 Proceedings of the 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet
The Journal of Machine Learning Research
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
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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In this paper, a novel feature selection method based on the normalization of the well-known mutual information measurement is presented. Our method is derived from an existing approach, the max-relevance and min-redundancy (mRMR) approach. We, however, propose to normalize the mutual information used in the method so that the domination of the relevance or of the redundancy can be eliminated. We borrow some commonly used recognition models including Support Vector Machine (SVM), k-Nearest-Neighbor (kNN), and Linear Discriminant Analysis (LDA) to compare our algorithm with the original (mRMR) and a recently improved version of the mRMR, the Normalized Mutual Information Feature Selection (NMIFS) algorithm. To avoid data-specific statements, we conduct our classification experiments using various datasets from the UCI machine learning repository. The results confirm that our feature selection method is more robust than the others with regard to classification accuracy.