{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T15:14:34Z","timestamp":1721661274272},"reference-count":6,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2001,12]]},"abstract":"<jats:p> Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be \"shared\" across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by extracting rules at two levels: hREX extracts rules by examining the hidden unit to class assignments while mREX extracts rules based on the input space to output space mappings. The rules extracted by our algorithm are compared and contrasted against a competing local rule extraction system. The central claim of this paper is that local function networks such as radial basis function (RBF) networks have a suitable architecture based on Gaussian functions that is amenable to rule extraction. <\/jats:p>","DOI":"10.1142\/s1469026801000305","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T10:58:59Z","timestamp":1027767539000},"page":"369-382","source":"Crossref","is-referenced-by-count":17,"title":["THE EXTRACTION AND COMPARISON OF KNOWLEDGE FROM LOCAL FUNCTION NETWORKS"],"prefix":"10.1142","volume":"01","author":[{"given":"KENNETH","family":"MCGARRY","sequence":"first","affiliation":[{"name":"School of Computing, Engineering and Technology, University of Sunderland, St Peters Campus, St Peters Way, Sunderland, SR6 ODD, England"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"STEFAN","family":"WERMTER","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Technology, University of Sunderland, St Peters Campus, St Peters Way, Sunderland, SR6 ODD, England"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JOHN","family":"MACINTYRE","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Technology, University of Sunderland, St Peters Campus, St Peters Way, Sunderland, SR6 ODD, England"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,1,25]]},"reference":[{"key":"p_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.728352"},{"key":"p_8","doi-asserted-by":"publisher","DOI":"10.1109\/72.712154"},{"issue":"1","key":"p_13","first-page":"62","volume":"2","author":"McGarry K.","year":"1999","journal-title":"Neural Computing Surveys"},{"key":"p_18","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)00064-S"},{"key":"p_19","first-page":"321","volume":"14","author":"Shavlik J.","year":"1994","journal-title":"Machine Learning"},{"key":"p_20","first-page":"105","author":"Sun R.","year":"2000","journal-title":"Proceedings of the IEEE International Joint Conference on Neural Networks, pages"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026801000305","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T13:50:25Z","timestamp":1565185825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026801000305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001,12]]},"references-count":6,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,1,25]]},"published-print":{"date-parts":[[2001,12]]}},"alternative-id":["10.1142\/S1469026801000305"],"URL":"https:\/\/doi.org\/10.1142\/s1469026801000305","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2001,12]]}}}