dataesr/scientific-paragraphs-categorization huggingface dataset:
The main advantage of this study is that the neural network model is optimized using swarm intelligence to improve accuracy. Mohammed et al. (2018)  presents experiments comparing many ML techniques. Scalability was tested when working with highly imbalanced massive datasets. RF, Balanced Bagging Ensemble (BBE) with DT, and Gaussian Naïve Bayes (GNB) were used. The results show that many detection algorithms performed well with medium-sized datasets but struggled to maintain similar predictions when dealing with massive data. They deduced that a balanced bagging ensemble with the hybrid balancing technique has superior prediction and that RF is scalable and capable of detecting fraud with good accuracy. Another use of bagging was suggested in  Akila and Srinivasulu Reddy (2018)  in the form of a cost-sensitive Risk Induced Bayesian Inference Bagging model termed RIBIB.
Padmanabhuni et al. (2019)  suggest ensemble learning composed of five individual machine learning classifiers: SVM, KNN, logR, NN, DT. They compared the final result with individual ML models in addition to other learning techniques such as Adaboost, RF, NN and Probabilistic Neural Network (PNN). The suggested model has higher accuracy than the other models considered in the study (82. 47%) but has lower sensitivity than Adaboost, RF, NN, and PNN. Thennakoon et al. (2019)  proposes a real-time fraud detection system that uses Principal Component Analysis (PCA) for dimensionality reduction. The authors also used 10-fold crossvalidation. To test their model, they used a private dataset containing fraudulent transaction logs combined with non-fraudulent transactions. The model was tested with four machine learning algorithms (SVM, NB, KNN, and logR).
However, it is not clear how the real-time feature was achieved, and no testing was carried out to prove the transactions were checked in real time. In  Kim et al. (2019) , the authors introduced a framework to compare the champion and challenger models, where the former is a hybrid ensemble model used for a credit card fraud detection system in a partner bank, and the latter is based on deep learning (see Section 5). The ensemble model is based on decision trees, logistic regressions, and shallow neural networks. The proposed model was evaluated in a real system. Additionally, the authors compared both models (champion and challenger) using offline testing and post-launch on a real dataset. Testing results showed that the best performance was achieved using the deep learningbased model (challenger).
In  Rtayli and Enneya (2020) , the authors proposed a credit card fraud detection based on a hybrid approach using the Support Vector Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method to select the predictive features, and the Hyper-Parameters Optimization (HPO) method to estimate the best hyperparameter values for the RF. The approach was validated based on three datasets and the results showed high accuracy performance compared to existing models. Ali Yes ßilkanat et al. (  2020 ) used a Gradient Boosting Tree (GBT) model to model a real-time credit card fraud detection system. The system works on streaming CNP transactions. For feature engineering, the authors combined many features, including numerical, hand-crafted numerical, categorical, and textual. The main contributions are the aggregation of categorical values and the use of the word embedding model.
The system uses RF and M-class Support Vector Machine (M-class SVM) in order to classify the collected data as legitimate or suspect. It consists of two phases, the first of which uses RF to classify users' operation features when they make payments (for example time, display balance, change password) into legitimate and fraudulent. Second, it uses M-class SVM to classify transaction features, such as account number, amount, and type, to detect fraud actions. The model is easy to implement, but the performance of the system can degrade if there are many trees in the RF that slow the algorithm and make it unsuitable for realtime detection  (Speiser et al. , 2019). Tran and Dang (2021)  solved the data imbalance problem (see Section 6) and and compared four machine learning algorithms (RF, logR, KNN, DT) to evaluate the performance of the obtained balanced dataset.
The results showed better performance with a balanced dataset than with a skewed imbalance dataset. Similarly, (RB and KR, 2021) investigated the use of multiple algorithms, such as SVM, KNN and ANN, to detect fraudulent credit card transactions. A comparison of the algorithms led to the conclusion that the ANN algorithm outperforms the other machine learning algorithms based on accuracy. The suggested model is however very simple, with no details in terms of the data preprocessing, normalization and sampling. The testing was only based on accuracy, precision and recall, and it is important to consider additional metrics for verification. In  Sudha and Akila (2021) , the behaviour of users of a banking website was used to classify transactions as either genuine or fraudulent. The operational and transactional features are combined into a single feature in a model with three phases.
1%. To validate the proposed solution, the authors also applied the new feature engineering method to Adaboost and logistic regression classifiers and showed the stability of this increase. The main benefit of this work is the improvement of the feature engineering phase. Baabdullah et al. (2020)  proposed a credit card fraud detection approach to solve problems related to imbalanced data using different techniques. This model uses two levels for data resampling (oversampling and undersampling). Additionally, to improve classification performance, several supervised algorithms, namely logR, NB, DT, RF, and KNN, are applied over the resampled datasets. Finally, to evaluate the performance, the performance evaluation metrics for the five classification models were compared with those from different datasets.
Bagga et al. (2020)  evaluates and compares the performance of nine techniques (logR, NB, RF, KNN, Multi Layer Perceptron (MLP), Adaboost, Quadrant Discriminant Analysis, etc. ) with pipelining and ensemble learning on the credit card fraud detection problem. The pipelining was used for feature selection. Ensemble learning was used through the bagging classifier. To overcome the imbalanced data the ADASYN method was applied. However, the model has an accuracy of 100%, which may be due to an overfitting problem. More testing is required with different datasets. In  Cochrane et al. (2021) , the authors presented a pattern analysis for attributes related to fraud with a view to applying indicators to increase the accuracy of fraud detection systems. It was found that some attributes are strongly correlated with fraud activities, showing that a transaction is fraudulent.
After building the correlation chart, this information can be applied to the training model. Two public datasets that simulate transactions were used to train and test the model. Furthermore, three machine learning algorithms were used on both datasets (DT, linR, logR) to build the predictive and prescriptive analysis. The results from the predictive models were then combined to determine whether or not the transaction is fraudulent. However, further experiments with private financial data are needed in order to improve the accuracy of fraud detection and to increase the speed of the system. Ingole et al. (2021)  proposed a credit card fraud detection system that focuses on orchestrating various services using the Oracle SOA suite. They tested different machine learning models  (supervised, unsupervised, traditional, and deep).
Whilst the suggested model is simple and easy to understand and implement, no details about the dataset, normalization phase, and testing were provided. The model was trained only once, and it is not clear how it can take into account the data drift issue. In addition, the comparative study of the literature was not justified. In  Cui et al. (2021) , the authors presented an anomaly detection mechanism to detect online banking fraud. The system is designed to solve problems related to previous fraud detection systems namely the limited amount of historical data for users, the skewed nature of transaction data and the absence of a uniform way of treating users' attribute values. The authors presented a ranking metric embedding system called (ReMEMBeR), which uses multicontextual behaviour to reduce the false positive and error rate.
If the classification is efficient, then the days are similar; otherwise, it means that there is a covariate shift between these days. To characterize data, they are presented in a distance matrix to clarify the covariate shift. The RF algorithm is then used to cluster the distance matrix. After clustering, the authors observed four similar clusters that differ from others, namely: working days, school holidays, Saturdays and Sundays. The data shift was integrated as a new feature in the credit card fraud detection system. The main advantage is that the proposed model solves the problem of the variety in users' purchase behaviours and fraudulent mechanisms, since it changes over time. However, the dataset used is relatively old (from 2015), whereas fraudulent strategies have evolved over the past seven years.
Though the authors claim to have achieved superior per- formance compared with some state-of-the-art methods, the F1score is very low and the detection accuracy needs to be increased  Choi and Lee (2018). In addition, more metrics are required to better evaluate the model. Roy et al. (2018)  assessed the efficacy of four deep learning algorithms: RNN, GRU, LSTM, and ANN. Feature engineering was performed by adding new features to the ones provided in the dataset. It is worth noting that the dataset contains 80 million anonymous transactions collected during 8 months and provided by a financial institution. The sensitivity metric was used to discover the hyper-parameters that have the highest impact on the model performance. The authors concluded that the model performance is affected by the network size: the larger the network, the better the performance.
Babu and Pratap (2020) suggested using CNN to solve the fraud detection problem. The authors used a public dataset that already relies on PCA to hide sensitive data. Though the model achieved a high level of accuracy, it did not solve the skewness problem. This model is simple and fast in terms of computation time as it does not use any derivative variables or high-dimensional inputs. The model was tested with and without the max pooling layer, concluding that the accuracy decreases when the max pooling layer is applied. Meanwhile, only the accuracy was considered to assess the model while the dataset is highly imbalanced which may bias the results. Thus, more appropriate metrics should be applied to check the performance of the solution. Moreover, no comparison with similar works was provided.
In  Forough and Momtazi (2021) , the authors proposed an ensemble model for credit card fraud detection using two sequential classification models, LSTM and GRU networks. The outputs of these models are aggregated and fed to a multi-layer FNN as a voting method to predict fraud transactions. The authors claimed that this approach is the first to use an ensemble of sequential models in fraud detection. The model was validated based on two real datasets and compared against two baseline deep sequential models, solo LSTM and solo GRU, and a third ensemble model from the literature. The results showed that the proposed ensemble method outperforms the previous three models. The authors did not discuss how to handle imbalanced data by using the Area Under the Curve of Precision-Recall (AUC-PR) in their comparison.
This was selected for its applicability to highly class-imbalanced settings (see Section 7). Another scheme using LSTM to perform sequence classification is proposed in  Jurgovsky et al. (2018). It utilizes the LSTM network to aggregate the historical behaviour of users in order to improve the accuracy of fraud detection. This model was tested with a real dataset and compared with RF. The results showed that LSTM improves fraud detection and shows good performance in faceto-face transactions. However, it does not improve online ecommerce transactions  (Lucas et al. , 2020). Additionally, this solution requires manual feature construction before classification, which may not be suitable for large-scale and complex realworld detection systems  (Cheng et al. , 2020). In  Kewei et al. (2021) , the authors used a deep learning architecture that comprises three hidden layers consisting of 512, 256, and 32 neurons.
They combined several techniques, including feature engineering, memory compression, mixed precision, and ensemble loss to boost the performance of the suggested model. They used aggregated features and emphasized the hour feature as it impacts user behaviour. Experiments show that the suggested model outperforms traditional machine-learning-based methods that were tested, such as Naive Bayes and SVM. Ingole et al. (2021)  used CNN to classify fraudulent transactions as part of a service-oriented system. Though CNN is usually used on image datasets, the authors also showed that it gives good results on FDS. Ingole et al. (2021)  also used autoencoders as an unsupervised deep learning classifier in their model which comprises several other classifiers. Autoencoders were used for anomaly detection where it is trained with genuine transactions.
When a fraudulent transaction is fed into the model, it fails to regenerate at the output layer, and the transaction is thus considered fraud. The final solution is implemented using REST-API. However, REST services have security concerns compared to SOAP services. In  Zhou et al. (2021) , the authors proposed an Internet financial fraud detection for big data using Node2Vec, which is a semisupervised graph algorithm to represent nodes and edges. Node2-Vec represents the features in a low-dimensional vector using random walk. The large dataset was processed on Apache Spark GraphX clusters and Hadoop. The proposed approach consists of four main modules: 1. Reprocessing to clear data, remove redundancy and remove empty fields; 2. Sampling normal data features by dividing the data into parts and analyzing each part to collect the normal features; 3.
Class imbalance solutions Data imbalance occurs when the dataset has an unequal distribution of classes. The class imbalance can be an inherent property or caused by difficulty in collecting data due to the high cost, privacy concerns, and effort required  (Elrahman and Abraham, 2014). Credit card transactions commonly comprise an imbalanced dataset, including very few fraudulent transactions compared to genuine ones. To overcome this issue, a range of methods are suggested, namely, under-sampling  (Yen and Lee, 2006)  and oversampling  (Chawla et al. , 2002). However, these solutions are challenging because the credit card datasets are highly imbalanced, and instances of the dataset individually carry relevant information (for example transactions owned by the same cardholder)  (Ali Yes ßilkanat et al.
Oversampling Techniques In  Benchaji et al. (2018) , the authors propose using a genetic algorithm along with K-mean clustering to generate new input data from the minority class (fraudulent instances). This will produce a more balanced dataset and thus enhance the accuracy of the system. An autoencoder is also used for feature selection to generate discriminative features for fraudulent instances. However, the authors discussed the theory of each algorithm used more than the suggested solution itself and did not provide evidence for the proposed model. In  Dornadula and Geetha (2019) , the authors developed profiles for cardholders and used Synthetic Minority Oversampling (SMOTE) and One-class SVM to handle the imbalanced data. They measured the performance using Matthews correlation coefficient (MCC) which is used to deal with imbalanced datasets (see 7).
Similarly,  Rtayli and Enneya (2020)  used oversampling to solve the class imbalance issue and proposed a hybrid system that uses SMOTE. In  Tran and Dang (2021) , the authors tried to increase the effectiveness of machine learning models to detect credit card fraud by utilizing two resampling techniques for imbalanced datasets: SMOTE and daptive Synthetic (ADASYN), which is an improvement of SMOTE. The authors employed four machine learning algorithms (RF, logR, KNN, and DT) to compare and evaluate the performance of the balanced dataset produced from both SMOTE and ADASYN. However, the resampling method used might have led to overfitted data. Similarly,  Bagga et al. (2020)  used ADASYN to solve the class imbalance. However, they achieved an accuracy of 100%, which may indicate overfitting caused by the used oversampling technique.
In  Yang et al. (2019) , SMOTE was also used as an oversampling technique to balance the dataset. The performance testing of the model on a large-scale real dataset showed that the federated learning achieves an average of test Area Under the Curve (AUC) to 95. 5%, which is about 10% higher than traditional systems. However, this scheme needs to take reliable measurements into consideration in order to preserve privacy  (Li et al. , 2020). Ingole et al. (2021)  oversampled their data set with varying sample sizes before reaching 20,000 samples to achieve the best performance. They used the sklearn. util. resample utility which is based on bootstrapping. The suggested solution is simple but might lead to overfitting issues. It is also tedious to find the oversampling rate for larger datasets as it requires testing several sampling sizes.
Undersampling Techniques As discussed in Section 5,  (Roy et al. , 2018)  compared several deep learning models. In addition, they solved the class imbalance and scalability problems through random undersampling. They tested several ratios and found that 10:1 non-fraudulent: fraudulent was the most suitable for credit card detection. However, this result is highly dependent on the dataset used. Li et al. (2021)  proposed a method to handle the class imbalance with overlap based on a divide-and-conquer approach. They trained an anomaly detection model on the minority class in order to exclude a few outliers of the minority class and many samples from the majority class. The remaining samples, therefore, constitute an overlapping subset with a low imbalance ratio. Afterwards, they dealt with the resulting overlapping subset by using a nonlinear classifier to distinguish samples.
They also proposed a new assessment criterion called Dynamic Weighted Entropy (DWE) to evaluate the quality of the set, which takes into account the trade-off between the number of excluded outliers of the minority class and the ratio of class imbalance of the overlapping subset. et al. (2018)  tested Random Oversampling (ROS) with many classifiers for massive datasets. They also tested various types of SMOTE, namely the original version, the borderline1 and borderline2, SVM-SMOTE, SMOTEENN, and SMOTETomek using RUS. BBE was tested, which is balanced internally with Random Undersampling (RUS) along with SMOTE, to build a hybrid balancing model. The results showed that the hybrid techniques are scalable and achieved the best performance. Thennakoon et al. (2019)  suggests a fraud detection system that uses oversampling and undersampling techniques to solve the data imbalance problem.
It uses SMOTE to over-sample fraudulent instances, as well as condensed nearest neighbor CNN and RUS to undersample the genuine records. Baabdullah et al. (2020)  performed a comparative experimental study to detect credit card fraud, and to tackle the imbalance classification problem by applying different machine learning algorithms to handle imbalanced datasets along with over-and under-sampling methods. The study allows experimental insights to be obtained into the use of ML techniques to detect card fraud, especially with imbalanced datasets. The authors used SMOTE as an oversampling technique and NearMiss as an undersampling technique.
Other Balancing Techniques Alongside over-sampling, under-sampling, and hybrid techniques that combine both over-and under-sampling, many researchers have suggested other solutions to handle the class imbalance issue, such as cost-sensitive learning and extreme learning. Cost-sensitive learning assigns different costs to the types of misclassification errors. Afterwards, specialized methods are used to take these costs into account. Cost-sensitive learning aims to minimize the cost of a model on a training dataset rather than minimizing the prediction error, as in regular learning  (Sammut and Webb, 2010). Moreover, parallel one-class extreme learning (P-ELM) has been suggested in  Li et al. (2018)  to solve the class imbalance problem. It combines one-class classifiers and Extreme Learning Machine (ELM).
The suggested solution is applicable to both multi-class and binary classification problems. The one-class ELM relies on the fact that all trained objects should be in a feature space that contains just the target class, thus being useful for outlier detection when only the target class data is available  (Leng et al. , 2015). P-ELM addresses the class imbalance problem using the Bayesian theory. The training data set is divided into k subsets based on the class features, where k is the number of classes. The resulting datasets are then fed in parallel to separate Gaussian kernelbased one-class ELM, then the conditional probability is used to determine the output class. Another ELM-based solution for the imbalance issue has been proposed in  Xiao et al. (2017). It uses class-specific cost regulation extreme learning machine (CCR-ELM) and deals with binary and multi-class classification problems.
The value of 60% was selected based on multiple experiments and is recommended as a minimum threshold to avoid internal imbalance. Another paper that addressed the class imbalance is  Fu et al. (2016) , with cost-based sampling applied to the CNN classifier. According to  Zong et al. (2013) , WELM is well-suited for imbalanced datasets. By assigning different weights for each example, the weighted ELM can be generalized to cost-sensitive learning  (Zong et al. , 2013). Some research has focused on using WELM in credit card detection, mainly to deal with the imbalance problem. Zhu et al. (2020)  applied multiple optimization techniques to optimize a WELM and compared their performance to the credit card imbalanced classification problem. The experimental results show that WELM with a dandelion algorithm (i.
Dataset Many datasets have been used in the literature to test and validate credit card fraud detection models. Generally, researchers rely on private datasets. However, there are publicly available datasets that could be also used. It is worth noting that the availability of a well-established dataset is important to allow researchers provide a clear comparative study of their results and thus show the superiority of a given solution over another. Since the availability of such a dataset is still an issue in the credit card fraud detection field, as most banks refuse to share their data because of privacy concerns and the sensitivity of customers' data, finding datasets for financial research is difficult. As a result, some studies have examined the creation of synthetic datasets  (Dandekar et al.