Modeling insurance fraud detection using imbalanced data classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099094" target="_blank" >RIV/61989100:27240/16:86099094 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86099094

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-27400-3_11" target="_blank" >http://dx.doi.org/10.1007/978-3-319-27400-3_11</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-27400-3_11" target="_blank" >10.1007/978-3-319-27400-3_11</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling insurance fraud detection using imbalanced data classification

Popis výsledku v původním jazyce

This paper proposes an innovative insurance fraud detection method to deal with the imbalanced data distribution. The idea is based on building insurance fraud detection models using Decision tree (DT), Support vector machine (SVM) and Artificial Neural Network (ANN), on data partitions derived from under-sampling (with-replacement and without-replacement) of the majority class and merging it with the minority class. Throughout the paper, ten-fold cross validation method of testing is used. Its originality lies in the use of several partitioning under-sampling approaches and choosing the best. Results from a publicly available automobile insurance fraud detection data set demonstrate that DT performs slightly better than other algorithms, so DT model was used to compare between different partitioning-under-sampling approaches. Empirical results illustrate that the proposed model gave better results. (C) Springer International Publishing Switzerland 2016.

Název v anglickém jazyce

Modeling insurance fraud detection using imbalanced data classification

Popis výsledku anglicky

This paper proposes an innovative insurance fraud detection method to deal with the imbalanced data distribution. The idea is based on building insurance fraud detection models using Decision tree (DT), Support vector machine (SVM) and Artificial Neural Network (ANN), on data partitions derived from under-sampling (with-replacement and without-replacement) of the majority class and merging it with the minority class. Throughout the paper, ten-fold cross validation method of testing is used. Its originality lies in the use of several partitioning under-sampling approaches and choosing the best. Results from a publicly available automobile insurance fraud detection data set demonstrate that DT performs slightly better than other algorithms, so DT model was used to compare between different partitioning-under-sampling approaches. Empirical results illustrate that the proposed model gave better results. (C) Springer International Publishing Switzerland 2016.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 419

ISBN

978-3-319-27399-0

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

117-127

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Pietermaritzburg

Datum konání akce

1. 12. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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