Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification Mohammed, Mohammed Falah and Lim, Chee Peng 2017, Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification, Applied soft computing journal, vol. 52, pp. 135-145, doi: 10.1016/j.asoc.2016.12.001. Attached Files Name Description MIMEType Size Downloads Title Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification Author(s) Mohammed, Mohammed Falah Lim, Chee Peng orcid.org/0000-0003-4191-9083 Journal name Applied soft computing journal Volume number 52 Start page 135 End page 145 Total pages 11 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2017-03 ISSN 1568-4946 Keyword(s) Fuzzy min-max model pattern classification hyperbox structure neural network learning Summary An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers. Language eng DOI 10.1016/j.asoc.2016.12.001 Field of Research 0102 Applied Mathematics 0801 Artificial Intelligence And Image Processing 0806 Information Systems Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2016, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30093345 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, 11:41:35 EST Thu, 06 Apr 2017, 05:07:11 EST Tue, 06 Jun 2017, 06:04:31 EST Wed, 07 Jun 2017, 11:19:34 EST Wed, 07 Jun 2017, 11:21:00 EST Wed, 07 Jun 2017, 11:21:41 EST Sat, 22 Jan 2022, 01:31:31 EST Filtered Full Citation counts:   Cited 19 times in TR Web of Science Cited 24 times in Scopus Search Google Scholar Access Statistics: 365 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Wed, 05 Apr 2017, 11:41:35 EST