Friday, 28 August 2020

MATLAB code of Support vector machine combined with Particle swarm optimization

The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in an acceptable classification accuracy. Feature selection is of great importance in pattern classification, medical data processing, machine learning, and data mining applications. Therefore, a good feature selection method based on the number of features investigated for sample classification is needed in order to speed up the processing rate, predictive accuracy, and to avoid incomprehensibility. In this project, particle swarm optimization (PSO) is used to implement a feature selection, and support vector machines (SVMs) with the one-versus-rest method serve as a fitness function of PSO for the classification problem. 

YouTube Video of the Project: 


Building an efficient classification model for classification problems with different dimensionality and different sample size is important. The main tasks are the selection of the features and the selection of the classification method. In this project, we used PSO to perform feature selection and then evaluated fitness values with a SVM, which was combined with the one-versus-rest method.

MATLAB Implementation of the Project:

Fig: PSO VSM Blank GUI

Fig: GA VSM 

Fig: PSO VSM 

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