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A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble
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