Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy

2018 ◽  
Vol 42 (10) ◽  
Author(s):  
R. Karthikeyan ◽  
P. Alli
Keyword(s):  
Feature Selection ◽  
Diabetic Retinopathy ◽  
Support Vector Machines ◽  
Parameters Optimization ◽  
Support Vector ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Vector Machines

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

2012 ◽  
Vol 57 (5) ◽  
Author(s):  
Mohammad Reza Daliri
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Particle Swarm ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Binary Particle Swarm Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

Wavelet twin support vector machines based on glowworm swarm optimization

2017 ◽  
Vol 225 ◽  
pp. 157-163 ◽  
Author(s):  
Shifei Ding ◽  
Yuexuan An ◽  
Xiekai Zhang ◽  
Fulin Wu ◽  
Yu Xue
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Swarm Optimization ◽  
Twin Support Vector Machines ◽  
Glowworm Swarm Optimization ◽  
Vector Machines

scholarly journals Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data

2010 ◽  
Vol 48 (7) ◽  
pp. 2880-2889 ◽  
Author(s):  
Björn Waske ◽  
Sebastian van der Linden ◽  
Jón Atli Benediktsson ◽  
Andreas Rabe ◽  
Patrick Hostert
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Vector Machines
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