A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble
Neural and Evolutionary Computing
2019-07-09 v1
Abstract
This paper presents a k-means-based multi-subpopulation particle swarm optimization, denoted as KMPSO, for training the neural network ensemble. In the proposed KMPSO, particles are dynamically partitioned into clusters via the k-means clustering algorithm at every iteration, and each of the resulting clusters is responsible for training a component neural network. The performance of the KMPSO has been evaluated on several benchmark problems. Our results show that the proposed method can effectively control the trade-off between the diversity and accuracy in the ensemble, thus achieving competitive results in comparison with related algorithms.
Cite
@article{arxiv.1907.03743,
title = {A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble},
author = {Hui Yu},
journal= {arXiv preprint arXiv:1907.03743},
year = {2019}
}