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Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques

Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques

Sharmila Subudhi, Suvasini Panigrahi
Copyright: © 2018 |Volume: 5 |Issue: 3 |Pages: 20
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522547037|DOI: 10.4018/IJRSDA.2018070101
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MLA

Subudhi, Sharmila, and Suvasini Panigrahi. "Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques." IJRSDA vol.5, no.3 2018: pp.1-20. http://doi.org/10.4018/IJRSDA.2018070101

APA

Subudhi, S. & Panigrahi, S. (2018). Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques. International Journal of Rough Sets and Data Analysis (IJRSDA), 5(3), 1-20. http://doi.org/10.4018/IJRSDA.2018070101

Chicago

Subudhi, Sharmila, and Suvasini Panigrahi. "Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques," International Journal of Rough Sets and Data Analysis (IJRSDA) 5, no.3: 1-20. http://doi.org/10.4018/IJRSDA.2018070101

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Abstract

This article presents a novel approach for fraud detection in automobile insurance claims by applying various data mining techniques. Initially, the most relevant attributes are chosen from the original dataset by using an evolutionary algorithm based feature selection method. A test set is then extracted from the selected attribute set and the remaining dataset is subjected to the Possibilistic Fuzzy C-Means (PFCM) clustering technique for the undersampling approach. 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.

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