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)
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Consistency measures for feature selection
Journal of Intelligent Information Systems
Feature Selection Using Mutual Information: An Experimental Study
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Feature selection with dynamic mutual information
Pattern Recognition
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
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
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With rapid development of information technology, dimensionality of data in many applications is getting higher and higher. However, many features in the high-dimensional data are redundant. Their presence may pose a great number of challenges to traditional learning algorithms. Thus, it is necessary to develop an effective technique to remove irrelevant features from data. Currently, many endeavors have been attempted in this field. In this paper, we propose a new feature selection method by using conditional mutual information estimated dynamically. Its advantage is that it can exactly represent the correlation between features along with the selection procedure. Our performance evaluations on eight benchmark datasets show that our proposed method achieves comparable performance to other well-established feature selection algorithms in most cases.