Credit Card Fraud Detection using Non-Overlapped Risk based Bagging Ensemble (NRBE) – NLPIR自然语言处理与信息检索共享平台

自然语言处理与信息检索共享平台 自然语言处理与信息检索共享平台

Credit Card Fraud Detection using Non-Overlapped Risk based Bagging Ensemble (NRBE)

NLPIR SEMINAR Y2019#2

INTRO

       In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Wednesdays, and each time a keynote speaker will share understanding of papers published in recent years with you.

Arrangement

This week’s seminar is organized as follows:
1. The seminar time is 1.pm, Wed., at Zhongguancun Technology Park ,Building 5, 1306.
2. The lecturer is Yvette, the paper’s title is Curriculum Learning for Natural Answer Generation.
3. The seminar will be hosted by Baohua Zhang.
4. Attachment is the paper of this seminar, please download in advance.

Anyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.

Credit Card Fraud Detection using Non-Overlapped Risk based Bagging Ensemble (NRBE)

S. Akila      U. Srinivasulu Reddy

Abstract

       Fraud due to credit card misuse costs consumers several billions of dollars annually. This is due to the huge usage levels and inability of the systems to automatically detect the anomalies. This paper analyzes the implicit nature of data with noise and imbalance and proposes a Non-overlapped Risk based Bagged Ensemble model (NRBE) to handle imbalance and noise contained in the credit card transactions. The bagging model has been enhanced in terms of a novel bag creation model and an effective risk based base learner. Non-overlapped bag creation generates training subsets to handle data imbalance and the risk based Na?ve Bayes eliminates the issues arising due to noise. Experiments were conducted and comparisons were performed with existing state-of-the-art fraud detection models, which indicates that NRBE exhibits improved performances of 5% in terms of BCR and BER, 50% in terms of Recall and 2X to 2.5X times reduced cost.

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