{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:37:43Z","timestamp":1770683863430,"version":"3.49.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2016,7,26]],"date-time":"2016-07-26T00:00:00Z","timestamp":1469491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2016,9]]},"abstract":"<jats:p>Data mining and machine learning methods have been utilized successfully in the past for identifying and forecasting meaningful patterns from data repositories of diverse application domains. However, the high number of dimensions and instances present in large datasets pose great technical challenges to these existing methods of classification and prediction. The presence of noisy data and missing values makes it even tougher to achieve accurate prediction outcomes. A number of hybrid methodologies constituting dimensionality reduction, feature selection and noise removal methods have been proposed in the literature. However, majority of these techniques force the analysts to compromise on accuracy of classification and prediction results. Therefore, there is a strong need of a methodology that not only scales well with the sheer size and volume of data but also provides near to accurate classification and prediction results by effectively handling the noise in data variables. This paper proposes a fuzzy-based methodology which ranks the dimensions in order of importance and exploits Fuzzy Nearest Neighbor (FNN) approaches for accurate classification and prediction. An experimental evaluation on real world datasets, taken from UCI machine learning repository, shows that the proposed approach outperforms the existing classification and prediction methods by employing only a subset of important features to achieve high prediction accuracy rates at multiple levels of data abstraction.<\/jats:p>","DOI":"10.3233\/jifs-152176","type":"journal-article","created":{"date-parts":[[2016,7,26]],"date-time":"2016-07-26T10:02:02Z","timestamp":1469527322000},"page":"1759-1768","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A fuzzy-based methodology for accurate classification and prediction in large datasets"],"prefix":"10.1177","volume":"31","author":[{"given":"Muhammad","family":"Usman","sequence":"first","affiliation":[{"name":"Department of Computing, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan"}]},{"given":"M.","family":"Usman","sequence":"additional","affiliation":[{"name":"Department of Computing, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan"}]},{"given":"Sohail","family":"Asghar","sequence":"additional","affiliation":[{"name":"Deparment of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan"}]}],"member":"179","published-online":{"date-parts":[[2016,7,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.11.008"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.05.031"},{"key":"e_1_3_1_4_2","first-page":"199","article-title":"A Prototype System for Educational Data Warehousing and Mining","author":"Dimokas N.","year":"2008","unstructured":"DimokasN., et al., A Prototype System for Educational Data Warehousing and Mining, 2008, IEEE, pp. 199\u2013203.","journal-title":"IEEE"},{"key":"e_1_3_1_5_2","unstructured":"UsmanM. PearsR. and FongA. Data guided approach to generate multi-dimensional schema for targeted knowledge discovery 2012."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.10.057"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.08.028"},{"key":"e_1_3_1_8_2","article-title":"A conceptual model for multi-level mining and visualization of association rules","author":"Usman M.","year":"2014","unstructured":"UsmanM., and AhmadW., A conceptual model for multi-level mining and visualization of association rules, in Ninth International Conference on Digital Information Management (ICDIM), 2014.","journal-title":"Ninth International Conference on Digital Information Management (ICDIM)"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.07.001"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.01.006"},{"key":"e_1_3_1_11_2","first-page":"229","article-title":"Data guided approach to generate multidimensional schema for targeted knowledge discovery","author":"Usman M.","year":"2012","unstructured":"UsmanM., PearsR., and FongA.C.M., Data guided approach to generate multidimensional schema for targeted knowledge discovery, in Tenth Australasin Data Mining Conference (AusDM,12), 2012, pp. 229\u2013240.","journal-title":"Tenth Australasin Data Mining Conference (AusDM,12)"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.4156\/ijact.vol2.issue3.4"},{"key":"e_1_3_1_13_2","article-title":"A methodology for integrating and exploiting data mining techniques in the design of data warehouses","author":"Usman M.","year":"2010","unstructured":"UsmanM., and PearsR., A methodology for integrating and exploiting data mining techniques in the design of data warehouses, in Advanced Information Management and Service (IMS), 6th International Conference on, 2010.","journal-title":"Advanced Information Management and Service (IMS), 6th International Conference on"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"MissaouiR. et al. Toward Integrating Data Warehousing with Data Mining Techniques Data warehouses and OLAP: Concepts architectures and solutions 2007 p. 253.","DOI":"10.4018\/987-1-59904-364-7.ch011"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74553-2_18"},{"key":"e_1_3_1_16_2","first-page":"1","article-title":"OLEMAR: An on-line environment for mining association rules in multidimensional data","volume":"2","author":"Messaoud R.B.","year":"2007","unstructured":"MessaoudR.B., et al., OLEMAR: An on-line environment for mining association rules in multidimensional data, Advances in Data Warehousing and Mining, IGI Global2 (2007), 1\u201335.","journal-title":"Advances in Data Warehousing and Mining, IGI Global"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-006-0046-2"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(99)00142-9"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2011.05.040"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-18302-7_4"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2012.6251337"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.10.038"},{"key":"e_1_3_1_23_2","unstructured":"AsuncionA. and NewmanD.J. UCI machine learning repository [http:\/\/archive.ics.uci.edu\/ml] Irvine CA: University of California School of Information and Computer Science 2010."},{"issue":"6","key":"e_1_3_1_24_2","first-page":"01","article-title":"Performance evaluation of different data mining classification algorithm and predictive analysis","volume":"10","author":"Shazmeen S.F.","year":"2013","unstructured":"ShazmeenS.F., BaigM.M.A., and PawarM.R., Performance evaluation of different data mining classification algorithm and predictive analysis, Journal of Computer Engineering10(6) (2013), 01\u201306.","journal-title":"Journal of Computer Engineering"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.4236\/jsea.2015.89045"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11812-3_25"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asw.2014.09.002"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.03.041"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.5120\/3371-4657"},{"key":"e_1_3_1_30_2","unstructured":"KohaviR. and BeckerB. Adult dataset. 1996; Available from: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Adult."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.ivs.9500072"},{"key":"e_1_3_1_32_2","unstructured":"BlackardJ.A. DeanD. and AndersonC. The forest covertype dataset 1998."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-152176","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-152176","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-152176","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T07:20:39Z","timestamp":1770621639000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-152176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,26]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["10.3233\/JIFS-152176"],"URL":"https:\/\/doi.org\/10.3233\/jifs-152176","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,26]]}}}