Cost-sensitive metaheuristic technique for credit card fraud detection

Singh Ajeet; Jain Anurag

摘要

To prevent online financial losses, credit card frauds need to be addressed and it should be controlled. To minimize the credit card frauds, fraudulent credit card transactions should be identified and minimized. Keeping this fact in view, in this paper, a cost-sensitive metaheuristic technique (CSFPA: cost-sensitive learning flower pollination metaheuristic algorithm) is proposed to minimize the misclassification cost of credit card transactions from class imbalance data using the correlation-based feature section method (CFS), flower pollination algorithm (FPA), and cost-sensitive classifier. To identify the fraudulent transactions, the random forest (RF) ensemble classifier is used as a base learner in the costsensitive classifier for classification tasks and the proposed CSFPA technique has been tested on the Brazilian bank dataset. The performance of proposed CSFPA has been compared with cost insensitive techniques and cost-sensitive techniques such as NB (Naive Base), KNN (K-Nearest Neighbors), LB (Logit Boost), CSForest (Cost-sensitive decision forest), NBBFS (NB Best First Search) KNNGSS (KNN Greedy Stepwise Search), and CSFCS (CSForest Cuckoo Search). The experimental outcomes revealed that the proposed CSFPA technique has obtained encouraging outcomes for handling misclassification costs (Total cost and Avg. cost) and also has outperformed (FPR, Precision and Recall) compared to all the other approaches.

机译：为防止在线财务损失，需要解决信用卡欺诈，并应控制。为了最大限度地减少信用卡欺诈，应识别和最小化欺诈性信用卡交易。在此文中保持这一事实，提出了一种成本敏感的成分技术（CSFPA：成本敏感的学习花卉授粉成群质算法），以最大限度地使用基于相关性的特征部分从类别不平衡数据中的信用卡交易错误分类成本方法（CFS），花授粉算法（FPA）和成本敏感分类器。为了识别欺诈性交易，随机林（RF）合奏分类器用作分类任务的成本抑制分类器中的基本学习者，并且在巴西银行数据集上已经过测试了CSFPA技术。已提出的CSFPA的性能与成本不敏感的技术和成本敏感技术（如NB（天真碱），KNN（K-CORMALBORE），LB（LOGIT BOOST），CSFOREST（成本敏感的决定林），NBBF）（ NB最佳第一搜索）KNNGSS（KNN贪婪逐步搜索），以及CSFCS（CSFOREST CUCKOO Search）。实验结果表明，拟议的CSFPA技术已经获得了令人鼓舞的成果，以处理错误分类成本（总成本和成本），与所有其他方法相比，也表现优于（FPR，Precision和Recall）。

Cost-sensitive metaheuristic technique for credit card fraud detection

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