Decision Templates based Ensemble Classifiers for Automobile Insurance Fraud Detection (original) (raw)

2019 Global Conference for Advancement in Technology (GCAT), 2019

Abstract

In this paper, we have removed the class imbalance problem using SMOTE and used Decision Templates ensemble technique for efficiently detecting the auto insurance fraud. We employed three supervised classifiers namely, Support Vector Machine, Multilayer Perceptron and K-nearest Neighbors for classification purpose. The final classification results are obtained by aggregating the results obtained from the classifiers using Decision Templates technique. Our model has been experimentally evaluated with a real –world automobile insurance dataset.

Suvasini Panigrahi hasn't uploaded this paper.

Let Suvasini know you want this paper to be uploaded.

Ask for this paper to be uploaded.