{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T13:08:46Z","timestamp":1716383326885},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2005,9]]},"abstract":"<jats:p> A robust method, fuzzy kNNModel, for toxicity prediction of chemical compounds is proposed. The method is based on a supervised clustering method, called kNNModel, which employs fuzzy partitioning instead of crisp partitioning to group clusters. The merits of fuzzy kNNModel are two-fold: (1) it overcomes the problems of choosing the parameter \u03b5 \u2014 allowed error rate in a cluster and the parameter N \u2014 minimal number of instances covered by a cluster, for each data set; (2) it better captures the characteristics of boundary data by assigning them with different degrees of membership between 0 and 1 to different clusters. The experimental results of fuzzy kNNModel conducted on thirteen public data sets from UCI machine learning repository and seven toxicity data sets from real-world applications, are compared with the results of fuzzy c-means clustering, k-means clustering, kNN, fuzzy kNN, and kNNModel in terms of classification performance. This application shows that fuzzy kNNModel is a promising method for the toxicity prediction of chemical compounds. <\/jats:p>","DOI":"10.1142\/s1469026805001635","type":"journal-article","created":{"date-parts":[[2006,1,12]],"date-time":"2006-01-12T07:03:15Z","timestamp":1137049395000},"page":"321-333","source":"Crossref","is-referenced-by-count":3,"title":["FUZZY kNNMODEL APPLIED TO PREDICTIVE TOXICOLOGY DATA MINING"],"prefix":"10.1142","volume":"05","author":[{"given":"GONGDE","family":"GUO","sequence":"first","affiliation":[{"name":"Department of Computing, University of Bradford, Bradford, West Yorkshire BD7 1DP, UK"}]},{"given":"DANIEL","family":"NEAGU","sequence":"additional","affiliation":[{"name":"Department of Computing, University of Bradford, Bradford, West Yorkshire BD7 1DP, UK"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","volume-title":"Predictive Toxicology","author":"Friksson L.","year":"2004"},{"key":"rf2","volume-title":"Predictive Toxicology","author":"Parsons S.","year":"2004"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1021\/ci025585q"},{"key":"rf4","unstructured":"M. V.\u00a0Craciun, Intelligent Systems in Medicine, ed. H. N.\u00a0Teodorescu (Performantica, Iasi, Romania, 2004)\u00a0pp. 61\u201369."},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1080\/01969727308546046"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"rf13","first-page":"580","volume":"15","author":"Keller J. M.","journal-title":"IEEE Trans. Syst. Man Cybernet."},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1080\/105172397243079"},{"key":"rf18","volume-title":"Data Mining: Practical Machine Learning Tools with Java Implementations","author":"Witten I. H.","year":"2000"},{"key":"rf21","volume-title":"Statistical Methods","author":"Snedecor G. W.","year":"1989"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026805001635","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T11:18:45Z","timestamp":1565176725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1469026805001635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005,9]]},"references-count":10,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,20]]},"published-print":{"date-parts":[[2005,9]]}},"alternative-id":["10.1142\/S1469026805001635"],"URL":"https:\/\/doi.org\/10.1142\/s1469026805001635","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2005,9]]}}}