Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Statistics for Engineering and the Sciences (5th Edition)
Statistics for Engineering and the Sciences (5th Edition)
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Evolving pattern recognition systems
IEEE Transactions on Evolutionary Computation
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis
IEEE Transactions on Evolutionary Computation
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Learning polynomial feedforward neural networks by genetic programming and backpropagation
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
Mutation-based genetic neural network
IEEE Transactions on Neural Networks
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
Towards collaborative feature extraction for face recognition
Natural Computing: an international journal
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Projection techniques are frequently used as the principal means for the implementation of feature extration and dimensionality reduction for machme learing applications. A well established and broad class of such projection techniques is the projection pursuit (PP). Its core design parameter is a projection index, which is the driving force in obtaining the transformation function via optimization, and represents in an explicit or implicit way the user's perception of the useful information contained within the datasets. This paper seeks to address the problem related to the design of PP index functions for the linear feature extraction case. We achieve this using an evolutionary search framework, capable of building new indices to fit the properties of the available datasets. The high expressive power of this framework is sustained by a rich set of function primitives. The performance of several PP indices previously proposed by human experts is compared with these automatically generated indices for the task of classification, and results show a decrease in the classification errors.