A PSO Based Adaboost Approach to Object Detection
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Supervised projection approach for boosting classifiers
Pattern Recognition
Expert Systems with Applications: An International Journal
Intelligent visual recognition and classification of cork tiles with neural networks
IEEE Transactions on Neural Networks
Evolutionary discriminant feature extraction with application to face recognition
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Particle swarm optimization based AdaBoost for face detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Optimizing data transformations for classification tasks
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Shorter, more reliable and valid tests by means of a genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Evolving linear transformations with a rotation-angles/scaling representation
Expert Systems with Applications: An International Journal
Weighted principal component extraction with genetic algorithms
Applied Soft Computing
Supervised subspace projections for constructing ensembles of classifiers
Information Sciences: an International Journal
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An evolutionary approach to the supervised reduction of dimensions is introduced in this paper. Traditionally, such reduction has been accomplished by maximizing one or another measure of class separation. Quite often, the rank deficiency of the involved covariance matrices precludes the application of this classical approach to real situations. Besides, the number of projections cannot be chosen freely, but it is bounded to be equal to the number of classes minus one. By contrast, our evolution strategy reduces dimensions by the direct minimization of the number of misclassified patterns. No matrices are involved whatsoever and the number of projections can be chosen without restrictions. This allows to obtain two-dimensional renderings of data sets with more than three classes such as the 19 class UCI soybean problem. A nonlinear generalization of this procedure based on the hierarchical composition of linear projections is shown to solve the UCI thyroid problem with state of the art recognition rates.