Improving Nearest Neighbor Partitioning Neural Network Classifier Using Multi-layer Particle Swarm Optimization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 923)


Nearest neighbor partitioning (NNP) method has been proved to be an effective method to enhance the quality of neural network classifiers. However, there are many cluster shapes in NNP, which results in a large number of local optimal solutions in the searching space by the traditional particle swarm optimization (PSO) algorithm. Therefore, the multi-layer particle swarm optimization (MLPSO) is introduced to increase the diversity of searching groups through increasing the number of layers, thereby improving the performance when facing with large scale problems. In this study, we adopt the combination of multi-layer particle swarm optimization and nearest neighbor partitioning to solve the local optimal problem caused by multi-cluster shapes in the optimization of NNP. Experimental results show that this method improves the performance of classifier.


Classification Nearest neighbor partitioning Neural network Multi-layer particle swarm optimization 



This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 61872419, No. 61873324, No. 81671785, No. 61672262. Science and technology project of Shandong Province under Grant No. 2015GGX101025. Project of Shandong Province Higher Educational Science and Technology Program under Grant no. J16LN07. Shandong Provincial Key R&D Program under Grant No. 2016ZDJS01A12, No. 2016GGX101001. Taishan Scholar Project of Shandong Province, China.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.School of InformaticsLinyi UniversityLinyiChina
  3. 3.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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