Software change prediction using voting particle swarm optimization based ensemble classifier

2017 ◽  
Author(s):  
Ruchika Malhotra ◽  
Megha Khanna
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Ensemble Classifier ◽  
Swarm Optimization ◽  
Software Change ◽  
Change Prediction

scholarly journals A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 255
Author(s):  
Gui-Rong You ◽  
Yeou-Ren Shiue ◽  
Wei-Chang Yeh ◽  
Xi-Li Chen ◽  
Chih-Ming Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ensemble Classifier ◽  
Optimization Approach ◽  
Classifier Ensembles ◽  
Swarm Optimization ◽  
Factors Affecting

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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