A New Hyperbox Selection Rule and a Pruning Strategy for the Enhanced Fuzzy Min–Max Neural Network
In this paper, we extend our previous work on the Enhanced Fuzzy Min–Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16555/ http://umpir.ump.edu.my/id/eprint/16555/ http://umpir.ump.edu.my/id/eprint/16555/ http://umpir.ump.edu.my/id/eprint/16555/1/A%20new%20hyperbox%20selection%20rule%20and%20a%20pruning%20strategy%20for%20the%20enhanced%20fuzzy%20min%E2%80%93max%20neural%20network.pdf |
Internet
http://umpir.ump.edu.my/id/eprint/16555/http://umpir.ump.edu.my/id/eprint/16555/
http://umpir.ump.edu.my/id/eprint/16555/
http://umpir.ump.edu.my/id/eprint/16555/1/A%20new%20hyperbox%20selection%20rule%20and%20a%20pruning%20strategy%20for%20the%20enhanced%20fuzzy%20min%E2%80%93max%20neural%20network.pdf