A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble
This work addresses the challenge of optimizing neural network ensembles for improved performance, but it appears incremental as it builds on existing particle swarm optimization techniques.
The paper tackled the problem of balancing diversity and accuracy in neural network ensembles by proposing a k-means-based multi-subpopulation particle swarm optimization (KMPSO) method, achieving competitive results on benchmark problems.
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.