Journal ArticleDOI
Research into a Feature Selection Method for Hyperspectral Imagery Using PSO and SVM
TLDR
A novel feature selection and classification method for hyperspectral images is proposed by combining the global optimization ability of particle swarm optimization (PSO) algorithm and the superior classification performance of a support vector machine (SVM).About:
This article is published in Journal of China University of Mining and Technology.The article was published on 2007-12-01. It has received 45 citations till now. The article focuses on the topics: Linear classifier & Feature (computer vision).read more
Citations
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Journal ArticleDOI
A novel particle swarm optimization algorithm with Levy flight
Huseyin Hakli,Harun Uğuz +1 more
TL;DR: Experimental results show that the LFPSO is clearly seen to be more successful than one of the state-of-the-art PSO (SPSO) and the other PSO variants in terms of solution quality and robustness and compared with well-known and recent population-based optimization methods.
Journal ArticleDOI
An enhanced particle swarm optimization with levy flight for global optimization
R. Jensi,G. Wiselin Jiji +1 more
TL;DR: The enhancement involves introducing a levy flight method for updating particle velocity and the test proves that the proposed PSOLF method is much better than SPSO and LFPSO.
Journal ArticleDOI
A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine
TL;DR: The results show that feature selection technique using BPSO is more successful than feature selection techniques using GA on determining CAD existence, with more little complexity of classifier system and more little classification time compared with whole features used SVM.
Journal ArticleDOI
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
Koushik Nagasubramanian,Sarah Jones,Soumik Sarkar,Asheesh K. Singh,Arti Singh,Baskar Ganapathysubramanian +5 more
TL;DR: In this paper, the authors used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally effective waveband combination for detecting charcoal rot infection in soybeans.
Journal ArticleDOI
Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach
TL;DR: The proposed SVR-PSO methodology has demonstrates an effective engineering strategy that can be applied in problems of structural and construction engineering prospective, applied to predict shear strength of steel fiber reinforced concrete beam using advanced hybrid artificial intelligence models developed in this study.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Journal ArticleDOI
Choosing Multiple Parameters for Support Vector Machines
TL;DR: The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.
Proceedings ArticleDOI
A combined SVM and LDA approach for classification
Tao Xiong,Vladimir Cherkassky +1 more
TL;DR: It is shown that existing SVM software can be used to solve the SVM/LDA formulation and empirical comparisons of the proposed algorithm with SVM and LDA using both synthetic and real world benchmark data are presented.
Journal ArticleDOI
Hyperspectral image data analysis
TL;DR: The article includes an example of an image space representation, using three bands to simulate a color IR photograph of an airborne hyperspectral data set over the Washington, DC, mall.