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The 10-fold cross validation method is then used on the balanced dataset for training and validating a group of Weighted Extreme Learning Machine (WELM) classifiers generated from various combinations of WELM parameters. Finally, the test set is applied on the best performing model for classification purpose. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world automobile insurance defraud dataset. Besides, a comparative analysis with another approach justifies the superiority of the proposed system.<\/p>","DOI":"10.4018\/ijrsda.2018070101","type":"journal-article","created":{"date-parts":[[2018,5,30]],"date-time":"2018-05-30T07:48:39Z","timestamp":1527666519000},"page":"1-20","source":"Crossref","is-referenced-by-count":13,"title":["Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques"],"prefix":"10.4018","volume":"5","author":[{"given":"Sharmila","family":"Subudhi","sequence":"first","affiliation":[{"name":"Veer Surendra Sai University of Technology, Burla, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suvasini","family":"Panigrahi","sequence":"additional","affiliation":[{"name":"Veer Surendra Sai University of Technology, Burla, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJRSDA.2018070101-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2016.04.007"},{"key":"IJRSDA.2018070101-1","doi-asserted-by":"publisher","DOI":"10.1111\/1539-6975.00022"},{"key":"IJRSDA.2018070101-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.insmatheco.2007.08.002"},{"key":"IJRSDA.2018070101-3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2013.06.023"},{"key":"IJRSDA.2018070101-4","doi-asserted-by":"publisher","DOI":"10.1016\/0098-3004(84)90020-7"},{"key":"IJRSDA.2018070101-5","article-title":"C4. 5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure.","volume":"Vol. 3","author":"N. 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