Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Axiomatic Approach to Feature Subset Selection Based on Relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
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
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Advanced Local Feature Selection in Medical Diagnostics
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
IEEE Transactions on Knowledge and Data Engineering
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A parameterless feature ranking algorithm based on MI
Neurocomputing
The ANNIGMA-wrapper approach to fast feature selection for neuralnets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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Feature selection processes improve the accuracy, computational efficiency and scalability of classification process in data mining applications. This paper proposes two filter and wrapper hybrid approaches for feature selection techniques by combining the filter's feature ranking score in the wrapper stage. The first approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The second hybrid combines an improved version of MI based (Maximum Relevance and Minimum Redundancy; MaxRel-MinRed) filter ranking heuristic with the wrapper heuristic ANNIGMA (MaxRel-MinRed-ANNIGMA). The novelty of our approach is that we integrate the capability of wrapper approach to find better feature subset by combining filter's ranking score with the wrapper-heuristic's score that take advantages of both filter and wrapper heuristics. The performances of the hybrid approaches have been verified using synthetic, bench mark data sets and real life data set and compared to both independent filter and wrapper based approaches. Experimental results show that hybrid approaches (MR-ANNIGMA and MaxRel-MinRed-ANNIGMA) achieve more compact feature sets and higher accuracies than filter and wrapper approaches alone.