Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection

Zhu, Honghao; Liu, Guanjun; Zhou, MengChu; Xie, Yu; Abusorrah, Abdullah; Kang, Qi

doi:10.1016/j.neucom.2020.04.078

Cited by 133 publications

(58 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, more advanced AI techniques have recently appeared: expert systems to detect fraud in the form of rules [19], pattern recognition to approximate classes or patterns of suspicious behavior [20], ML to automatically detect risky features [21], neural networks that can learn suspicious patterns from data [22], optimization of weighted extreme learning machines for imbalanced classification in credit card fraud detection [23], transaction fraud detection based on total order relation and behavior diversity [24], online fault detection models and strategies based on clouds [25], and deep representation learning with full center loss for credit card fraud detection [26].…”

Section: ML For Risky Websites Detectionmentioning

confidence: 99%

Knowledge-Based Approach to Detect Potentially Risky Websites

et al. 2021

View full text Add to dashboard Cite

Nowadays, fraudulent and malicious websites are emerging as a harmful and very common problem on the Internet. It causes huge money losses and irreparable damage for both companies and particulars. To face this situation, governments have approved multiple law projects. This way, the legality on the Internet is being enforced and sanctions to those offenders who develop illegal or malicious activities are being imposed. However, governments still need a way to simplify the classification of websites into risky or non-risky, since most of this work is manual. This paper presents the DOmains Classifier based on RIsky Websites (DOCRIW ) framework to detect domains that contain possible fraud or malicious content. It is based on two main components. The first component is a previously built knowledge base containing information from risky websites. The second one complements the system with a binary classifier able to label a website (as risky or not) considering just its domain. The system makes use of web information sources and includes host-based variables. It also applies similarity measures, supervised learning algorithms and optimization methods to enhance its performance. The presented work is experimental, rendering promising outcomes.INDEX TERMS Risky website detection, malware alerts, knowledge-based systems, similarity metrics, combination of information.

show abstract

Section: ML For Risky Websites Detectionmentioning

confidence: 99%

Knowledge-Based Approach to Detect Potentially Risky Websites

et al. 2021

View full text Add to dashboard Cite

show abstract

“…REMEDIAL-HwR-HUS decouples the highly concurrent labels and applies the undersampling processing. But there are several problems in these algorithms: (1) the algorithms do not fundamentally change their original 2 Scientific Programming disadvantages and may still cause serious overfitting or loss of information;…”

Section: Related Workmentioning

confidence: 99%

“…MeanIR. MeanIR represents the average level of imbalance in the dataset, as shown in equation (2). MeanIR is the mean of all labels IR:…”

Section: Ir(y) �mentioning

confidence: 99%

“…For example, in the field of tumor classification, nontumor patients are the majority class, while tumor patients are the minority class [1], but we are more concerned about the minority of tumor patients. ese problems also exist in the fields of medical imaging classification, credit card fraud [2] detection, and network intrusion identification, etc. e multilabel imbalance problem is different from the traditional imbalance problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Decoupling and Bidirectional Resampling Method for Multilabel Classification of Imbalanced Data with Label Concurrence

Zhou

Dong

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

Label imbalance is one of the characteristics of multilabel data, and imbalanced data seriously affects the performance of the classifiers. In multilabel classification, resampling methods are mostly used to deal with imbalanced problems. Existing resampling methods balance the data by either undersampling or oversampling, which causes overfitting and information loss. Resampling has a significant impact on the minority labels. Furthermore, the high concurrency of majority labels and minority labels in many instances also affects the performance of classification. In this study, we proposed a bidirectional resampling method to decouple multilabel datasets. On one hand, the concurrency of labels can be reduced by setting termination conditions for decoupling, and on the other hand, the loss of instance information and overfitting can be alleviated by combining oversampling and undersampling. By measuring the minority labels of the instances, the instances that have less impact on minority labels are selected to resample. The number of resampling is limited to keep the original distribution of the data during the resampling phase. The experiments on seven benchmark multilabel datasets have proved the effectiveness of the algorithm, especially on datasets with high concurrency of majority labels and minority labels.

show abstract

“…Zhu et al suggest an approach called Weighted Extreme Learning Machine (WELM) to solve imbalanced dataset problems [28]. WELM is a transformed version of ELM for imbalanced datasets assigning different weights to different types of samples [28].…”

Section: Imbalanced Dataset Problemmentioning

confidence: 99%

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

Yazıcı¹

2020

Computer Science &Amp; Information Technology

View full text Add to dashboard Cite

Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.

show abstract

Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection

Cited by 133 publications

References 46 publications

Knowledge-Based Approach to Detect Potentially Risky Websites

Knowledge-Based Approach to Detect Potentially Risky Websites

A Decoupling and Bidirectional Resampling Method for Multilabel Classification of Imbalanced Data with Label Concurrence

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

Contact Info

Product

Resources

About