Multi-objective K-means evolving spiking neural network model based on differential evolution
In this paper, a multi-objective K-means evolving spiking neural network (MO-KESNN) model based on differential evolution for clustering problems has been presented. K-means has been utilized to improve the ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better sol...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73471/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965142606&doi=10.1109%2fICCNEEE.2015.7381395&partnerID=40&md5=a9a14ac643ff1ce76bb86e428f54eede |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | In this paper, a multi-objective K-means evolving spiking neural network (MO-KESNN) model based on differential evolution for clustering problems has been presented. K-means has been utilized to improve the ESNN model. This model enhances the flexibility of the ESNN algorithm in producing better solutions which is used to overcome the disadvantages of K-means. Several standard data sets from UCI machine learning are used for evaluating the performance of this model. It has been found that MO-KESNN gives competitive results in clustering accuracy performance and the number of pre-synaptic neurons measure simultaneously compared to the standard K-means. More discussion is provided to prove the effectiveness of the new model in clustering problems. Clustering; Differential Evolution; Evolving Spiking Neural. |
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