Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Consistency-based search in feature selection
Artificial Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Wrapper for Feature Selection Based on Mutual Information
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Adaptive branch and bound algorithm for selecting optimal features
Pattern Recognition Letters
A genetic algorithm-based method for feature subset selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Feature selection based-on genetic algorithm for image annotation
Knowledge-Based Systems
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
A GA-SVM feature selection model based on high performance computing techniques
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Hyperspectral data selection from mutual information between image bands
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
An effective feature selection scheme via genetic algorithm using mutual information
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
A novel business cycle surveillance system using the query logs of search engines
Knowledge-Based Systems
A weighted twin support vector regression
Knowledge-Based Systems
A new automatic identification system of insect images at the order level
Knowledge-Based Systems
International Journal of Innovative Computing and Applications
A regularization for the projection twin support vector machine
Knowledge-Based Systems
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
Rule extraction from support vector machines based on consistent region covering reduction
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
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With the development and popularization of the remote-sensing imaging technology, there are more and more applications of hyperspectral image classification tasks, such as target detection and land cover investigation. It is a very challenging issue of urgent importance to select a minimal and effective subset from those mass of bands. This paper proposed a hybrid feature selection strategy based on genetic algorithm and support vector machine (GA-SVM), which formed a wrapper to search for the best combination of bands with higher classification accuracy. In addition, band grouping based on conditional mutual information between adjacent bands was utilized to counter for the high correlation between the bands and further reduced the computational cost of the genetic algorithm. During the post-processing phase, the branch and bound algorithm was employed to filter out those irrelevant band groups. Experimental results on two benchmark data sets have shown that the proposed approach is very competitive and effective.