A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network Mohammed, Mohammed Falah and Lim, Chee Peng 2017, A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network, Neural networks, vol. 86, pp. 69-79, doi: 10.1016/j.neunet.2016.10.012. Attached Files Name Description MIMEType Size Downloads Title A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network Author(s) Mohammed, Mohammed Falah Lim, Chee Peng orcid.org/0000-0003-4191-9083 Journal name Neural networks Volume number 86 Start page 69 End page 79 Total pages 11 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2017-02 ISSN 1879-2782 Keyword(s) Fuzzy min–max model Hyperbox structure Neural network learning Pattern classification Datasets as Topic Fuzzy Logic Neural Networks (Computer) Summary 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 of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems. Language eng DOI 10.1016/j.neunet.2016.10.012 Field of Research 080101 Adaptive Agents and Intelligent Robotics MD Multidisciplinary Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2016, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30093348 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 05 Apr 2017, 11:41:59 EST Thu, 06 Apr 2017, 05:07:15 EST Mon, 05 Jun 2017, 17:03:55 EST Tue, 06 Jun 2017, 06:04:33 EST Wed, 07 Jun 2017, 11:24:51 EST Wed, 07 Jun 2017, 11:26:52 EST Sat, 22 Jan 2022, 01:31:34 EST Filtered Full Citation counts:   Cited 21 times in TR Web of Science Cited 20 times in Scopus Search Google Scholar Access Statistics: 335 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 05 Jun 2017, 17:03:55 EST