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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...
Ensemble systems are composed of a set of individual classifiers, organized in a parallel way, that receive the input patterns and send their output to a combination method, which is responsible for providing the final output of the system. The use of feature selection methods in ensemble systems has been shown to be efficient, since it reduces the dimensionality while increases the diversity among...
The feature subset selection reduces the cost of collecting redundant features. It is the main goal of feature subset selection that generating a feature subset which can preserve the most useful information of the original features. The feature selection methods often need expensive cost to find the optimal feature subset. The asynchronous discrete particle swarm optimal search algorithm is proposed...
Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we...
Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle...
Automatic recognition of residential areas in high resolution images becomes one of hotspots in the remote sensing field. Feature selection of residential areas is crucial which affects the corresponding recognition results; however, it is very difficult to select optimal residential area features. Particle swarm optimization (PSO) is a new evolutionary computing technique which was developed through...
In feature-level fusion recognition system, there are two main missions. One is improving the recognition correct rate as soon as possible; the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general, the...
The feature subset selection is a key preprocessing part in the detection of the stored-grain insects based on the image recognition technology. According to the global optimization ability of the particle swarm optimization (PSO) and the superior classification performance of the support vector machines (SVM), this study proposed a method based on PSO and SVM to improve the classification accuracy...
Intelligent algorithms being applied in intrusion detection system (IDS) becomes a tendency in recent years. This paper presents a new method of hybrid detection based on BPSO-SVM, a mixed algorithm that is composed of modified binary particle swarm optimization (BPSO) and support vector machine (SVM). This algorithm proposes a simultaneous feature selection and SVM parameters optimization. Experiments...
Adaboost is an ensemble learning algorithm that combines many base-classifiers to improve their performance. Starting with Viola and Jonespsila researches, Adaboost has often been used to local feature selection for object detection. Adaboost by Viola-Jones consists of following two optimization schemes: (1) training of the local features to make base-classifiers, and (2) selection of the best local...
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