Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Classification by ensembles from random partitions of high-dimensional data
Computational Statistics & Data Analysis
Creating an ensemble of diverse support vector machines using adaboost
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen in order that the performance. Lots of tools have been developed to improve their performance, mainly the development of new classifying methods and the employment of ensembles. So, in this paper, our proposal is to use both the theory of ensembles and a genetic algorithm to enhance the LS-SVM classification. First, we randomly divide the problem into subspaces to generate diversity among the classifiers of the ensemble. So, we apply a genetic algorithm to find the values of the LS-SVM parameters and also to find the weights of the linear combination of the ensemble members, used to take the final decision.