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Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. This paper proposes an image classifier based on Support Vector Machine which related parameters are optimized by an improved Particle Swarm Optimization (PSO) algorithm. Because control parameters selection of PSO have no corresponding theoretical...
The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection...
Classification problem is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in classification, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques such as least Squares...
Reducing dimension processing is needed in feature samples because the repeated and secondary features would reduce the classification ability and increase computation complexity. In this paper, a feature selection method, named MPSO (Modified Particle Swarm Optimization), is proposed. The original group velocity of a particle swarm was changed into two separate and parallel particle swarm velocity,...
Fault diagnosis of electronic circuit is important for safety of the device and relevant power system. In the study, support vector regression (SVR) classifiers combined with the particle swarm optimization algorithm (POSA) are applied to construct diagnostic model of electronic circuit, and the diagnostic system structure of electronic circuit is presented on the basis of the model. It is powerful...
This paper presents a new method for hyperspectral image classification. It combines support vector machine (SVM), particle swarm optimization (PSO), and genetic algorithm (GA) together. Its aim is to improve the classification accuracy and reduce the computation consumption based on heuristic algorithms. Because the classification accuracy is impacted by the parameters of the SVM model and feature...
Classifier-based multivariate pattern recognition techniques have in recent years enabled highly sensitive mapping of brain regions where mind states can be decoded from functional magnetic resonance imaging (fMRI) data. The ??searchlight?? mapping approach, where the brain volume is exhaustively scanned with a fixed-size search volume, is highly appealing in terms of sensitivity but also exceedingly...
Unbalanced data, minority classes with few samples, present in many applications. It is difficult to solve the problems of unbalanced data by traditional methods. In this paper, a hybrid algorithm based on random over-sampling, decision tree (DT), particle swarm optimization (PSO) and feature selection is proposed to classify unbalanced data. The proposed algorithm has the ability to select beneficial...
This paper proposes an algorithm which combines Particle Swarm Optimization (PSO) with Least Squares Support Vector Machines (LSSVM) to identify lithology by using well logging data. First of all, PSO is used for optimizing the main parameters of LSSVM, and then by using the optimized parameters to obtain a better PSO-LSSVM classification model which can be used to identify lithology with logging...
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...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
This study investigates a new approach in hyperspectral image classification. The new method of hyperspectral classification in this paper is a new SVM algorithm based particle swarm optimization (PSO-SVM). First, three classification methods are used to classified a benchmark hyperspectral images: SVM, ML (maximum-likelihood) and K-nn (K-nearest neighbor), the performances of SVMs are compared with...
Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and...
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, increasing SVM classification accuracy. The study focuses...
A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of...
To improve the learning efficiency of support vector machine, an intelligent model selection scheme based on particle swarm optimization (PSO) was presented to optimize the hyper-parameters. By taking the model selection problem as a multi-object optimization problem, one can obtain a solution set known as Pareto front; each one model in this set is non-dominated. PSO was used to solve the above multi-objective...
Support vector machine for pattern classification is motivated by linear machines, but rely on preprocessing the data to represent in a high dimension with an appropriate nonlinear mapping, data from two categories can by separated by a hyperplane. To make certain the hyperplane, the key problem is selecting appropriate criterion and algorithm. To find out the appropriate solution vector in solution...
This paper proposed a feature selection strategy based on rough set theory (RST) and discrete particle swarm optimization (DPSO) methods prior to classify protein function. RST is adopted in the first phase with the aim to eliminate the insignificant features and prepared the reduce features to the next phase. In the second phase, the reduced features are optimized using the new evolutionary computation...
The least squares support vector machine (LSSVM) use quadratic loss function to replace the non-sensitive loss function and equality constraints to replace inequality constraints. LSSVM is widely used in pattern recognition and function regression, but its performance mainly depends on the parameters selection of it. Kernel parameter selection is very important, and which decide the fault diagnosis...
In this paper, we use particle swarm optimization with support vector machine optimized to evaluate the investment risk of electrical project. A hybrid intelligent system is applied to evaluation of electrical equipment, combining particle swarm optimize algorithm (PSO) and support vector machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general...
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