A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection

Li, Zhenchuan; Huang, Maosong; Liu, Guanjun; Jiang, Changjun

doi:10.1016/j.eswa.2021.114750

Cited by 107 publications

(38 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, (50) implies that ( 46) and ( 47) hold with probability P n at least 1 − 2M/n 3 if n > n 1 , which completes the proof.…”

Section: Proofsmentioning

confidence: 52%

“…Obviously, we have M i=1 η i (x)/π i ≥ M i=1 η i (x)/π = 1/π. This together with ( 60) and ( 61) yields that η w m − η u m ∞ ≤ (4(1 + M )/π) log n/n holds for 1 ≤ m ≤ M , which together with (50) implies that for all n > n 1 , (34) holds with probability P n at least 1 − 2M/n 3 . Therefore, for all n ≥ N 3 := max{n 1 , 2M }, there holds (34) with probability P n at least 1 − 1/n 2 .…”

Section: Let Us First Consider the Set Bmentioning

confidence: 76%

“…Imbalanced classification has been encountered in multiple areas such as telecommunication managements [8,4], bioinformatics [16,69], fraud detection [50,72], and medical diagnosis [93], and has been considered one of the top ten problems in data mining research [87,65]. In fact, the ratio of the size of the majority class to the minority class can be as huge as 10 6 [84].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Under-bagging Nearest Neighbors for Imbalanced Classification

Hang¹,

Cai²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

In this paper, we propose an ensemble learning algorithm called under-bagging k-nearest neighbors (under-bagging k-NN ) for imbalanced classification problems. On the theoretical side, by developing a new learning theory analysis, we show that with properly chosen parameters, i.e., the number of nearest neighbors k, the expected sub-sample size s, and the bagging rounds B, optimal convergence rates for under-bagging k-NN can be achieved under mild assumptions w.r.t. the arithmetic mean (AM) of recalls. Moreover, we show that with a relatively small B, the expected sub-sample size s can be much smaller than the number of training data n at each bagging round, and the number of nearest neighbors k can be reduced simultaneously, especially when the data are highly imbalanced, which leads to substantially lower time complexity and roughly the same space complexity. On the practical side, we conduct numerical experiments to verify the theoretical results on the benefits of the under-bagging technique by the promising AM performance and efficiency of our proposed algorithm.

show abstract

“…Thus, (50) implies that ( 46) and ( 47) hold with probability P n at least 1 − 2M/n 3 if n > n 1 , which completes the proof.…”

Section: Proofsmentioning

confidence: 52%

Section: Let Us First Consider the Set Bmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Under-bagging Nearest Neighbors for Imbalanced Classification

Hang¹,

Cai²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors of a previous study proposed a novel hybrid approach [14] utilizing a divide-and-conquer strategy for solving the issue of imbalanced classes. They trained a model of anomaly detection on the original dataset, and then they utilized a non-linear classifier for a complex subset.…”

Section: Related Workmentioning

confidence: 99%

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

et al. 2022

View full text Add to dashboard Cite

Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.

show abstract

“…In fraud detection, the number of fraud cases is normally tiny, as compared with those of normal transactions. Fraudsters always attempt to create a fraudulent transaction as close as possible to a real transaction, in order to avoid being detected (Li et al, 2021 ). This data imbalanced issue affects the performance of machine learning methods.…”

Section: Empirical Evaluationmentioning

confidence: 99%

An intelligent payment card fraud detection system

et al. 2021

View full text Add to dashboard Cite

Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

show abstract

A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection

Cited by 107 publications

References 23 publications

Under-bagging Nearest Neighbors for Imbalanced Classification

Under-bagging Nearest Neighbors for Imbalanced Classification

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

An intelligent payment card fraud detection system

Contact Info

Product

Resources

About