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Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
Proper parameter settings of support vector machine (SVM) and feature selection are of great importance to its efficiency and accuracy. In this paper, we propose a parallel adaptive particle swarm optimization algorithm to simultaneously perform the parameter optimization and feature selection for SVM, termed PTVPSO-SVM. It is implemented in an efficient parallel environment using PVM (Parallel Virtual...
Efficient feature selection is a key point in pattern classification. In this paper, we propose an improved feature selection method utilizing support vector machine approach based on recursive feature elimination (SVM-RFE) for multi-SVM classifier. This method uses class interval in SVM algorithm as the evaluation criterion, and eliminate features in a recursive way. And in this procedure, obtaining...
Support Vector Machine (SVM) is the focus of failure diagnose field. There is not a definite theory to guide the choice of its parameters. In this paper, the analysis and research is done to parameter optimization of SVM. The combined algorithm based on Quantum-behavior Particle Swarm Optimization (QPSO) and Simulated Annealing (SA) is present to optimize the parameters of SVM in order to improve...
In creating a pattern classifier, feature selection is often used to prune irrelevant and noisy features to producing effective features. Manually developing a feature set can be a very time consuming and costly endeavor. In this paper, an efficient feature selection algorithm based on improved binary particle swarm optimization and support vector machine Algorithm (IBPSO-SVM) was used. First a population...
Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples can lead inaccurate diagnosis of disease in clinic. Therefore, it has been shown that selecting a small set of marker genes can lead to improved classification accuracy. In this paper,...
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