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Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1722
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dc.contributor.authorPradeep, Ganghishetti
dc.contributor.authorRavi, Vadlamani
dc.contributor.authorNandan, Kaushik
dc.contributor.authorDeekshatulu, B.L.
dc.contributor.authorBose, Indranil
dc.contributor.authorAditya, A.
dc.date.accessioned2021-08-26T06:23:47Z-
dc.date.available2021-08-26T06:23:47Z-
dc.date.issued2015
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84946121728&doi=10.1007%2f978-3-319-20294-5_21&partnerID=40&md5=488c05a01ad531aa7b0c1e5210394ad7
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/1722-
dc.descriptionPradeep, Ganghishetti, Center of Excellence in CRM and Analytics, Institute for Development and Research in Banking Technology, Castle Hills Road #1 Masab Tank, Hyderabad, Andhra Pradesh 500057, India, SCIS, University of Hyderabad, Hyderabad,Andhra Pradesh, 500046, India; ; Ravi, Vadlamani, Center of Excellence in CRM and Analytics, Institute for Development and Research in Banking Technology, Castle Hills Road #1 Masab Tank, Hyderabad, Andhra Pradesh 500057, India; ; Nandan, Kaushik, Indian Institute of Technology, Patna, Bihar 800013, India; Deekshatulu, B.L., Center of Excellence in CRM and Analytics, Institute for Development and Research in Banking Technology, Castle Hills Road #1 Masab Tank, Hyderabad, Andhra Pradesh 500057, India; Bose, Indranil, IIM Calcutta, Kolkata, West Bengal 700104, India; Aditya, A., Center of Excellence in CRM and Analytics, Institute for Development and Research in Banking Technology, Castle Hills Road #1 Masab Tank, Hyderabad, Andhra Pradesh 500057, India
dc.descriptionISSN/ISBN - 03029743
dc.descriptionpp.239-250
dc.descriptionDOI - 10.1007/978-3-319-20294-5_21
dc.description.abstractIn this paper, we propose new rule based classifiers based on Firefly (FF) and Threshold Accepting (TA) Algorithms viz., Improved Firefly Miner, Threshold Accepting Miner, Hybridized Firefly-Threshold Accepting (FFTA) based Miner for classifying a company as fraudulent or non fraudulent with respect to their financial statements. We apply t-statistic based feature selection and investigate its impact on the results. FFTA and TA miners turned to be statistically similar. Both algorithms outperformed standard decision tree both in terms of sensitivity and the length of rules. © Springer International Publishing Switzerland 2015.
dc.publisherSCOPUS
dc.publisherLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.publisherSpringer Verlag
dc.relation.ispartofseries8947
dc.subjectEvolutionary computing rule miner and financial statement fraud detection
dc.subjectFirefly algorithm
dc.subjectThreshold accepting algorithm
dc.titleFraud detection in financial statements using evolutionary computation based rule miners
dc.typeConference Paper
Appears in Collections:Management Information Systems

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