Feature selection of hyperspectral data by considering the integration of Genetic Algorithms and Particle Swarm Optimization

2014 ◽  
Author(s):  
Pedram Ghamisi ◽  
Jon Atli Benediktsson
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Hyperspectral Data ◽  
Swarm Optimization ◽  
Selection Of

Identification of mammary lesions in thermographic images: feature selection study using genetic algorithms and particle swarm optimization

2019 ◽  
Vol 35 (3-4) ◽  
pp. 213-222 ◽  
Author(s):  
Amanda L. Rodrigues ◽  
Maíra A. de Santana ◽  
Washington W. Azevedo ◽  
Renato S. Bezerra ◽  
Valter A. F. Barbosa ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization

Particle Swarm Optimization for Object-Based Feature Selection of VHSR Satellite Images

2018 ◽  
Vol 15 (3) ◽  
pp. 379-383 ◽  
Author(s):  
Amin Alizadeh Naeini ◽  
Masoud Babadi ◽  
Sayyed Mohammad Javad Mirzadeh ◽  
Saied Amini
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Satellite Images ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Object Based ◽  
Selection Of

scholarly journals The Optimization of Feature Selection Based on Chaos Clustering Strategy and Niche Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Longzhen Duan ◽  
Shuqing Yang ◽  
Dongbo Zhang
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
High Dimensional ◽  
Swarm Optimization ◽  
New Model ◽  
Selection Algorithms ◽  
Np Problem ◽  
Selection Of ◽  
Better Than

With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved.

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