Elements of information theory
Elements of information theory
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction Using Information-Theoretic Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of feature selection techniques in bioinformatics
Bioinformatics
Consistency measures for feature selection
Journal of Intelligent Information Systems
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
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
Feature selection with dynamic mutual information
Pattern Recognition
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Learning to classify by ongoing feature selection
Image and Vision Computing
Conditional mutual information based feature selection for classification task
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Ensemble gene selection for cancer classification
Pattern Recognition
Estimating redundancy information of selected features in multi-dimensional pattern classification
Pattern Recognition Letters
Mutual information-based method for selecting informative feature sets
Pattern Recognition
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One of the challenges in data mining is the dimensionality of data, which is often very high and prevalent in many domains, such as text categorization and bio-informatics. The high-dimensionality of data may bring many adverse situations to traditional learning algorithms. To cope with this issue, feature selection has been put forward. Currently, many efforts have been attempted in this field and lots of feature selection algorithms have been developed. In this paper we propose a new selection method to pick discriminative features by using information measurement. The main characteristic of our selection method is that the selection procedure works like feature clustering in a hierarchically agglomerative way, where each feature is considered as a cluster and the between-cluster and within-cluster distances are measured by mutual information and the coefficient of relevancy respectively. Consequently, the final aggregated cluster is the selection result, which has the minimal redundancy among its members and the maximal relevancy with the class labels. The simulation experiments on seven datasets show that the proposed method outperforms other popular feature selection algorithms in classification performance.