An overview of mapping techniques for exploratory pattern analysis
Pattern Recognition
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Identifying core sets of discriminatory features using particle swarm optimization
Expert Systems with Applications: An International Journal
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
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In this work, a new technique for linear feature extraction and data projection using genetic algorithms (GA) is presented. GAs are employed to find linear projections in order to reduce the original number of features or to provide meaningful representations of the original data. The proposed technique is compared with well-known methods such as principal component analysis (PCA) and neural networks for non-linear discriminant analysis (NDA). A comparative study of these methods with several data sets is presented.