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Performance Evaluation of Credit Card Fraud Transactions using Boosting Algorithms

Kavya Divakar; Chitharanjan K.
In the era of digital world, internet has reached a global connectivity. The whole world has transformed into digital now. All the firms whether it is educational organizations, governmental organizations, shopping, businesses etc have turned into a digital format. With the increasing trend in online marketing, the credit card companies have rapidly expanded. Due to this makeover, fraudulent cases have started to grow up. Analyzing fraudulent transactions manually is time consuming and tedious. Hence, with the advent of internet technology like artificial intelligence, machine learning etc, it is possible to detect and predict the chances of fraudulent actions. To evaluate the model, a publicly available credit card dataset is used. By implementing traditional machine learning algorithms like naive bayes classifier, decision tree classifier, etc, the classifier which performs poorly is found out. Boosting algorithms AdaBoost, Gradient Boost and XGBoost are implemented to find out the one which performs more accurately and precisely to predict the fraudulent cases. By comparing the results, it was found out that XGBoost performs better.
Select Volume / Issues:
Year:
2019
Type of Publication:
Article
Keywords:
AdaBoost; Decision Tree Classifier; Gradient Boost; XGBoost
Journal:
IJECCE
Volume:
10
Number:
6
Pages:
262-270
Month:
November
ISSN:
2249-071X
Hits: 216

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