Particle swarm optimization-based ensemble learning for software change prediction

2018 ◽  
Vol 102 ◽  
pp. 65-84 ◽  
Author(s):  
Ruchika Malhotra ◽  
Megha Khanna
Keyword(s):  
Particle Swarm Optimization ◽  
Ensemble Learning ◽  
Particle Swarm ◽  
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.

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