A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Nonparametric selection of input variables for connectionist learning
Nonparametric selection of input variables for connectionist learning
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Effective Gene Selection Method Using Bayesian Discriminant Based Criterion and Genetic Algorithms
Journal of Signal Processing Systems
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
Efficient training of RBF neural networks for pattern recognition
IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
Fast generic selection of features for neural network classifiers
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Weighted feature extraction with a functional data extension
Neurocomputing
Feature Selection and Neural Network for analysis of microstructural changes in magnetic materials
Expert Systems with Applications: An International Journal
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This paper focuses on enhancing the effectiveness of filter feature selection models from two aspects. First, feature-searching engine is modified based on optimization theory. Second, a point injection strategy is designed to improve the regularization capability of feature selection. The second topic is important, because overfitting is usually experienced. To evaluate the proposed strategies, we implement these strategies to modify two classic filter feature selection models. One model is based on sequential forward search scheme and the other employs genetic algorithms (GA) for feature selection. Comparing the original and modified models on synthetic and real data, the contributions of our modification are shown.