A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

Abstract

With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.

[Retracted] Construction and Simulation of Online English Reading Model in Wireless Surface Acoustic Wave Sensor Environment Optimized by Particle Swarm Optimization

“…Piezoelectric surface wave devices are excellent electronic devices that emerged only in the mid-1960s and are developing rapidly. Since the speed of sound surface wave propagation in piezoelectric materials is 100,000 times slower than that of electromagnetic waves, the size of the device is greatly reduced compared to that of electromagnetic wave devices of the same frequency [8][9][10].…”

A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems

“…Many strategies have been suggested in the literature to minimize these two factors. There are two major modification strategies, the first one is the mutation of the particles' positions [5][6][7] and the second one is the tuning of PSO control parameters (ω, c l , and c g ) [8][9][10][11][12][13][14][15]. Some researchers used these two improvement strategies together to improve the performance of PSO [16,17].…”

“…Cauchy mutation strategy uses a scaling factor on the Cauchy mutation to control the distance the particle moves [7]. A detailed review with the evaluation of the mutation strategies is introduced in [5]. The mutation strategies slightly improve the premature convergence rate; however, they increase the convergence time and add complexity to the PSO strategy.…”

“…Clerck [9] concluded that the most suitable value of φ was 4.1, which showed many positive results. Therefore, from (5) and (6), ω = 0.729, and the author assumed c g = c l = 1.49445 [9]. Additionally, an empirical formula was introduced in [16] to examine the performance of the PSO search with the varying PSO control parameters.…”

“…The main inference from this study was that the balance between the acceleration parameters, c g and c l , does influence the regions of the parameter space that leads to approximately optimal performance. The relation between c g , c l , and ω is defined in (5). Additionally, this strategy introduced a relation between ω and (c g + c l ); however, the deterministic values of these parameters that can fit with all optimization problems were not introduced.…”