A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Digital image processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition and image analysis
Pattern recognition and image analysis
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Classification Based Similarity Metric for 3D Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Evaluating Feature Selection Methods for Learning in Data Mining Applications
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Genetic Algorithms for Feature Selection and Weighting, A Review and Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient huge-scale feature selection with speciated genetic algorithm
Pattern Recognition Letters
Integrated Computer-Aided Engineering
A new evolutionary particle filter for the prevention of sample impoverishment
IEEE Transactions on Evolutionary Computation
A new design method for linguistically understandable fuzzy classifier
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Integrated Computer-Aided Engineering
An efficient design of a nearest neighbor classifier for various-scale problems
Pattern Recognition Letters
A soft computing method for detecting lifetime building thermal insulation failures
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
A model for mining material properties for radiation shielding
Integrated Computer-Aided Engineering
Detection and classification of road signs for automatic inventory systems using computer vision
Integrated Computer-Aided Engineering
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This paper proposes a new pattern recognition scheme, combining a new adaptive feature weighting and modified k-Nearest Neighbor (k-NN) rule. The proposed feature weighting method named adaptive-3FW. It uses three non-uniform weight levels (zero weight, middle weight and full weight) to weight each feature. The middle weight value is determined using genetic algorithms (GAs). The proposed adaptive-3FW overcomes overfitting issues and achieves high recognition performance. Novel GA operators tailored for this formulation are introduced to implement the proposed scheme. Further, a modified k-NN is proposed which uses a class-dependent feature weighting strategy. Whilst the conventional pattern recognition systems use the same set of feature weights for all classes, the proposed algorithm uses different sets of feature weights for different classes. Experiments were performed with the UCI repository for machine learning databases and the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.