# Optimizing Feature Selection and Oversampling Using Metaheuristic Algorithms for Binary Fraud Detection Classification

Mariam M. Biltawi, Raneem Qaddoura, Hossam Faris

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Mariam M. Biltawi, Raneem Qaddoura, Hossam Faris. Optimizing Feature Selection and Oversampling Using Metaheuristic Algorithms for Binary Fraud Detection Classification. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.452-462, 10.1007/978-3-031-34111-3_38. hal-05449800

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# Optimizing Feature Selection and Oversampling Using Metaheuristic Algorithms for Binary Fraud Detection Classification

Mariam M. Biltawi<fnref n="1" />, Raneem Qaddoura<fnref n="1" />(✉), and Hossam Faris<fnref n="2" />,<fnref n="3" />

**Abstract.** Identifying fraudulent transactions and preventing unauthorized individuals from revealing credit card information are essential tasks for different financial entities. Fraud detection systems are used to apply this task by identifying the fraudulent transactions from the normal ones. Usually, the data used for fraud detection is imbalanced, containing many more instances of normal transactions than fraudulent ones. This causes diminished classification task results because it is hard to train a classifier that distinguishes between them. Another problem is caused by many features under study for the fraud detection task. This paper utilizes different metaheuristic algorithms for feature selection to solve the problem of unneeded features and uses the Synthetic Minority Oversampling Technique (SMOTE) to solve the imbalance problem of the data using different classification algorithms. The metaheuristic algorithms include Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and A Multi-Verse Optimizer (MVO), whereas the classification algorithms include Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The results show that applying the oversampling technique generated better results for the G-Mean and Recall values, while the feature selection process enhanced the results of almost all the classification algorithms.

**Keywords:** Fraud Detection · SMOTE · Oversampling · Classification · Metaheuristic Algorithms · Optimization

<footnote n="1">¹ Al Hussein Technical University, Amman 11831, Jordan
{mariam.biltawi,raneem.qaddoura}@htu.edu.jo</footnote>

<footnote n="2">² Research Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, Granada, Spain
inv.hfaris@ugr.es</footnote>

<footnote n="3">³ King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
hossam.faris@ju.edu.jo
http://evo-ml.com/</footnote>
# 1 Introduction

Fraud detection is the process of analyzing, identifying, and preventing unauthorized financial activities. Identify theft, fraudulent credit card transactions, and cyber hacking are examples of fraudulent activities. To avoid such fraudulent activities, different actions can be considered by targeted companies, such as providing employees training on fraud detection or incorporating fraud detection into websites [1]. Generally, fraud areas include bank fraud, insurance fraud, telecommunication fraud, and internet marketing fraud. Credit card fraud, which is the focus of this paper, is a subcategory of bank fraud, which can be transnational fraud or application fraud [1].

Credit card fraud occurs when someone's credit card information is acquired by an unauthorized user, which uses this information to perform purchases [3]. Recently, Machine Learning (ML) algorithms were used to help with credit card fraud detection through creating ML models [20], trained on datasets collected from real transactions. These models can then recognize unusual transactions and detect whether the credit card transaction is fraudulent or not [27].

Metaheuristic algorithms provide sufficiently good solutions to optimization problems through high-level procedures. The advantage of using metaheuristic algorithms is that they are considered generic optimizers that can be implemented not for a specific problem but for almost all problems [2]. That is, the metaheuristic algorithms find the most suitable solutions by encoding the candidate solution and applying this process to the different problems because finding the solutions is independent of the problem type [17]. Different metaheuristic algorithms can be found in the literature. However, these algorithms can be categorized using different taxonomies based on their characteristics. For example, population-based vs. single-point and nature-inspired vs. non-nature inspired. Moreover, metaheuristic algorithms can be used for feature selection and parameter tuning in fraud detection.

Feature selection is the process of selecting relevant features from the dataset to reduce noise and redundant data, thus, creating an effective model. Implementing feature selection's main advantages are simplifying the created models, reducing training time, and avoiding the curse of dimensionality problem [12]. Metaheuristic algorithms can work as optimization algorithms to select the most effective features for generating the model during the preprocessing step of the classification task [18]. Sometimes, experimented datasets contain imbalanced data, which means unequal distribution of examples in each class. To overcome such a problem, a researcher can perform oversampling or undersampling to equalize the number of examples in each class. Oversampling can be defined as the process of increasing the number of examples within the minimum class, while undersampling is the process of decreasing the number of examples within the maximum class [24].

This paper aims to optimize feature selection using metaheuristic algorithms for binary fraud detection classification with and without oversampling. The experimented oversampling method is Synthetic Minority Oversampling Technique (SMOTE). While the experimented metaheuristic algorithms for feature
selection are Particle Swarm Optimization (PSO) [10], Salp Swarm Algorithm (SSA) [13], Grey Wolf Optimizer (GWO) [15], and Multi-verse Optimizer (MVO) [14]. These metaheuristic algorithms are nature-inspired; for example, PSO is a well-known simple metaheuristic algorithm that mimics the behavior of swarms, SSA mimics salps, and GWO imitates the leadership hierarchy and hunting mechanism of grey wolves. While MVO is based on the concept of multi-verse. Moreover, three well-known classification algorithms were used to compare the results with and without feature selection. These algorithms are; Logistic Regression (LR) [4], Decision Tree (DT) [28], and Naive Bayes (NB) [16].

The remaining of this paper is organized as follows; Sect. 2 presents the related work, Sect. 3 presents the problem and data description, Sect. 4 illustrates the proposed approach with the feature selection methods using metaheuristic algorithms, Sect. 5 presents the experiments and results, and finally, Sect. 6 is the conclusion and future work.

## 2 Related Work

This section presents research papers experimenting with fraud detection, metaheuristic algorithms, oversampling techniques, and feature selection. The problem with the credit card fraud detection dataset is that it has only a few fraud examples compared to the non-fraud available examples, which reflect a highly imbalanced dataset, to solve this problem, Duman and Elikucuk [5] developed Migrating Birds Optimization algorithm (MBO) which is a neighborhood search technique. The authors also experimented with Genetic Algorithms (GA) and Scattered Search (SS) [6], and results showed that MBO outperformed GA and SS. Similarly, Jovanovic et al. [9] proposed a group search firefly metaheuristic algorithm to solve the problem of credit card fraud detection problem. Results showed that the proposed algorithm outperformed all other experimented algorithms.

Different metaheuristic algorithms were proposed for feature selection. For example, Taghian et al. [25] proposed a Wrapper-based Binary Sine Cosine Algorithm (WBSCA) for feature selection. WBSCA was experimented on seven UCI datasets and against Binary Bat Algorithm (BBA), and Binary Gravitational Search Algorithm (BGSA). The results showed that WBSCA is a competitive algorithm. On the other hand, Taghian et al. [26] conducted a comparison between three transfer function-based binary metaheuristic algorithms for feature selection; BBA, binary Grey Wolf Optimization (bGWO), and BGSA. Experimental results showed that BGSA outperformed both BBA and bGWO. Yusta [29] conducted a comparison, as well, between the three metaheuristic algorithms; Tabu Search, Greedy Randomized Adaptive Search Procedure (GRASP), and Memetic Algorithm, GA and traditional feature selection methods. Experimental results showed that Tabu Search and GRASP outperformed all other algorithms.

Metaheuristic algorithms for feature selection can be found in the literature. For example, Sayed et al. [23] worked on three phases, preprocessing the data,
performing feature selection, and building the classification model. In the pre-
processing phase, the authors implemented SMOTE for oversampling. Next, fea-
ture selection was implemented using the Chaotic Dragonfly Algorithm (CDA),
and then the Support Vector Machine (SVM) classifier was built and evaluated.
Moreover, Gharehchopogh et al. [8] experimented with different chaotic maps
to improve Vortex Search Algorithm (VSA) for feature selection on 24 UCI
datasets.

The difference between the current work and the work presented in this
section is that the current work focuses on comparing the results of using different
metaheuristic algorithms for feature selection, with and without oversampling,
rather than using only one metaheuristic algorithm. Moreover, this work also
experimented SSA algorithm in fraud detection, and to the best of the authors'
knowledge, has not been experimented with in fraud detection till now.

3 Problem and Data Description

The experimented dataset contains data from transactions made by credit cards of European cardholders within two days of September 2013 from the Kaggle website<fnref n="1" />. The number of fraud transactions is only 492 out of 284,807 transactions (around 0.002%), which makes this dataset highly imbalanced. The features of this dataset are the results of the Principal Component Analysis (PCA) transformation. The only original features are the time and amount, noting that the total number of features is 30. The class is represented with 1, which indicates the transaction is fraudulent, and 0, which indicates the opposite.

The credit card fraud detection problem includes building a model to detect
whether the transaction is fraudulent. Initially, this model should be trained
on past credit card transactions. The complexity of this problem depends on
different perspectives, such as the number of examples in the dataset within
each class and the number of features within the dataset.

In this paper, the credit card fraud detection dataset is highly imbalanced.
The solution of this problem can be solved using either oversampling or under-
sampling. However, oversampling was chosen because the number of fraud exam-
ples is very small compared to the non-fraud examples. On the other hand,
dimensionality reduction can be performed using feature selection methods. The
authors of this paper experimented with different metaheuristic algorithms for
feature selection.

4 Proposed Approach

The approach followed in this work is presented in Fig. 1. As observed from
the figure, the preprocessing step of the approach includes handling the missing
data, converting the categorical values to numerical ones, and oversampling the
dataset using the SMOTE technique. The second step, which is the main step

<footnote n="1">¹ https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud.</footnote>
of the approach, considers using the metaheuristic algorithms to optimize the classification process by selecting the features that give the best geometric mean (G-Mean) value for the classification model. This step works as follows:

1. A random population is generated for the first iteration of the optimization process. The population includes individuals with values indicating the existence or absence of each feature. The values direct the selection process of the features.

2. A classification model is generated for the training data with the selected features based on the values of each individual.

3. Fitness evaluation for the validation data using the complement of the G-Mean measure is used to evaluate the generated model. G-mean is commonly used to evaluate the quality of the model for imbalanced datasets.

4. The best individual with the fitness function's best value is recorded for the iteration.

5. The next iteration is performed by modifying the values of the individuals based on the metaheuristic algorithm to generate an enhanced population.

6. Step 2 to Step 5 is repeated until the maximum iteration is reached. This generates the best individual for the optimization process.

The last step includes evaluating the optimization process using the testing data by applying the classification process and evaluating the model using different evaluation measures: Precision, Recall, F1-score, and G-Mean.

# 5 Experiments and Results

The approach followed in this study is discussed in this section. The different settings are presented here with the results obtained from the experiment using the oversampling technique and the feature selection optimization of the metaheuristic algorithms.

Different classification algorithms, including the LR, NB, and DT with default parameters' values, and different metaheuristic algorithms, including PSO, SSA, GWO, and MVO, are used in the experiments. The EvoloPy framework [7] is extended to include the classification task and the feature selection optimization of the implemented metaheuristic algorithms. The scikit-learn Python library [19] is used for the classification algorithms and the precision, recall, and f1-score evaluation measures, while the imbalanced-learn Python library [11] is used for the SMOTE and the G-mean methods. Fifty individuals and fifty iterations are applied by each metaheuristic algorithm and 30 independent runs are considered.

Table 1 presents the fraud detection results for feature selection optimization using different metaheuristic algorithms and classification algorithms regarding Recall, Precision, F1-score, and G-Mean. The Recall values for the LR and NB show advanced results for all the metaheuristic algorithms compared to the results shown by these algorithms without applying the feature selection
<figure><img src="image_3.png" /><figcaption>Fig. 1. Feature selection using metaheuristic algorithms for fraud detection</figcaption></figure>

optimization. In contrast, the DT shows decreased Recall values for the meta-
heuristic algorithms. The precision and F1-score results show advancement for
the LR for all the metaheuristic algorithms but only show advanced results for
GWO and MVO for the DT and SSA, GWO, and MVO for the NB. In addition,
the G-Mean results show advancement for the different classification algorithms.
The SSA shows the best results compared to the other metaheuristic algorithms
for the DT in terms of G-Mean, while the MVO and GWO show the best results
for the LR and NB, respectively. The best results for the SSA and DT for the
G-Mean measure are obtained.
After applying the SMOTE oversampling technique, different observations are shown in Table 2. Advanced results are observed for all the metaheuristic algorithms for the Recall and G-Mean values, while decreased values are observed for all the metaheuristic algorithms for the Precision and F1-score. The reason behind this observation comes from the behavior of the metaheuristic algorithms, which use the fitness function to lead the solution to the best value, which affects the quality of the evaluation [21,22]. In this case, the fitness function prioritizes enhancing the G-Mean value with disregard for the Precision value. The Recall values are enhanced based on enhancing the G-Mean value by the fitness function, while the F1-score values are affected by the decreased values of the Precision. By looking into the G-Mean values, the different metaheuristic algorithms show different enhancements in the results by having the best values of the G-Mean for the GWO with LR, PSO with DT, and MVO with NB. The best results for the PSO and DT for the G-Mean measure are obtained.

Figures 2 and 3 show the convergence curves for the fitness function values without applying the oversampling and with applying it, respectively. It is observed from Fig. 2 that the best convergence is obtained by the MVO for the LR, SSA, and GWO for DT and GWO for NB. It is also observed that SSA has decreased strikingly in the early iterations for the LR and NB, while all the metaheuristic algorithms have a stability of the drop value to a fixed value before iteration number 20. MVO and GWO also have a dramatic decrease for the NB algorithm before iteration numbers 15 and 5, respectively. For the DT algorithm, GWO converged earlier than the SSA algorithm, but SSA obtained better fitness function values at later iterations. On the other hand, it is observed from Fig. 3 that GWO has the best values for the LR while MVO has the best results for NB. All the metaheuristic algorithms have achieved similar values at the latest iterations for DT but the GWO has descended more quickly than the other metaheuristic algorithms to achieve optimized value.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Convergence curves for the fitness function for LR, DT, and NB without oversampling.</figcaption></figure>
<table><caption>**Table 1.** Fraud detection results for feature selection optimization using different metaheuristic algorithms and different classification algorithms</caption><thead><tr><th colspan="6">Recall</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr></thead><tbody><tr><td>LR</td><td>0.500</td><td>0.837</td><td>0.860</td><td>0.860</td><td>0.884</td></tr><tr><td>DT</td><td>0.820</td><td>0.720</td><td>0.720</td><td>0.760</td><td>0.760</td></tr><tr><td>NB</td><td>0.820</td><td>0.905</td><td>0.881</td><td>0.833</td><td>0.881</td></tr><tr><th colspan="6">Precision</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr><tr><td>LR</td><td>0.581</td><td>0.766</td><td>0.771</td><td>0.787</td><td>0.792</td></tr><tr><td>DT</td><td>0.732</td><td>0.720</td><td>0.692</td><td>0.776</td><td>0.760</td></tr><tr><td>NB</td><td>0.149</td><td>0.132</td><td>0.197</td><td>0.313</td><td>0.230</td></tr><tr><th colspan="6">F1-Score</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr><tr><td>LR</td><td>0.538</td><td>0.800</td><td>0.813</td><td>0.822</td><td>0.835</td></tr><tr><td>DT</td><td>0.744</td><td>0.720</td><td>0.706</td><td>0.768</td><td>0.760</td></tr><tr><td>NB</td><td>0.252</td><td>0.231</td><td>0.322</td><td>0.455</td><td>0.365</td></tr><tr><th colspan="6">G-Mean</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr><tr><td>LR</td><td>0.707</td><td>0.8232</td><td>0.8129</td><td>0.8233</td><td>0.8436</td></tr><tr><td>DT</td><td>0.905</td><td>0.9393</td><td>0.9497</td><td>0.9495</td><td>0.9393</td></tr><tr><td>NB</td><td>0.252</td><td>0.9340</td><td>0.9460</td><td>0.9474</td><td>0.9463</td></tr></tbody></table>

<table><caption>**Table 2.** Fraud detection results for feature selection optimization using different metaheuristic algorithms and different classification algorithms with oversampling</caption><thead><tr><th colspan="6">Recall</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr></thead><tbody><tr><td>LR</td><td>0.500</td><td>0.950</td><td>0.917</td><td>0.967</td><td>0.967</td></tr><tr><td>DT</td><td>0.760</td><td>0.868</td><td>0.811</td><td>0.868</td><td>0.868</td></tr><tr><td>NB</td><td>0.820</td><td>0.860</td><td>0.880</td><td>0.880</td><td>0.860</td></tr><tr><th colspan="6">Precision</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr><tr><td>LR</td><td>0.581</td><td>0.073</td><td>0.057</td><td>0.060</td><td>0.081</td></tr><tr><td>DT</td><td>0.576</td><td>0.426</td><td>0.417</td><td>0.393</td><td>0.359</td></tr><tr><td>NB</td><td>0.139</td><td>0.076</td><td>0.086</td><td>0.087</td><td>0.087</td></tr><tr><th colspan="6">F1-Score</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr><tr><td>LR</td><td>0.538</td><td>0.136</td><td>0.108</td><td>0.112</td><td>0.149</td></tr><tr><td>DT</td><td>0.655</td><td>0.571</td><td>0.551</td><td>0.541</td><td>0.508</td></tr><tr><td>NB</td><td>0.237</td><td>0.139</td><td>0.156</td><td>0.159</td><td>0.158</td></tr><tr><th colspan="6">G-Mean</th></tr><tr><th>Algorithm</th><th>Without FS</th><th>FS-PSO</th><th>FS-SSA</th><th>FS-GWO</th><th>FS-MVO</th></tr><tr><td>LR</td><td>0.707</td><td>0.9521</td><td>0.9503</td><td>0.9541</td><td>0.9497</td></tr><tr><td>DT</td><td>0.871</td><td>0.9680</td><td>0.9677</td><td>0.9679</td><td>0.9678</td></tr><tr><td>NB</td><td>0.9000</td><td>0.9036</td><td>0.9041</td><td>0.9042</td><td>0.9043</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Convergence curves for the fitness function measure for LR, DT, and NB with oversampling.</figcaption></figure>

# 6 Conclusion and Future Work

This study experiments with different metaheuristic algorithms by optimizing the feature selection process of the fraud detection task using different classification algorithms, including the LR, DT, and NB. PSO, SSA, GWO, and MVO are selected as metaheuristic algorithms and the SMOTE oversampling technique is used to solve the imbalanced problem of the dataset. The results show that the different metaheuristic algorithms enhanced the classification task for most of the recorded results compared to applying the classification task without feature selection. Oversampling the dataset generated better results in terms of Recall and G-Mean compared with the results obtained without oversampling. The Precision and F1-score results were affected with decreased values when oversampling the dataset and applying the feature selection process.

For future work, the values of the Precision and F1-score can be enhanced by applying a multi-objective fitness function to evaluate the generated individuals at each iteration with G-Mean, Precision, Recall, and F1-score, and not only the G-Mean values. The approach followed in this study can be experimented with using different datasets for fraud detection. In addition, the optimization process can be extended to enhance the parameters' values for the different classification algorithms.

# References

1. Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. **68**, 90–113 (2016)

2. Abdel-Basset, M., Abdel-Fatah, L., Sangaiah, A.K.: Metaheuristic algorithms: a comprehensive review. In: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, pp. 185–231 (2018)

3. Chaudhary, K., Yadav, J., Mallick, B.: A review of fraud detection techniques: credit card. Int. J. Comput. Appl. **45**(1), 39–44 (2012)

4. Cox, D.R.: The regression analysis of binary sequences. J. Roy. Stat. Soc. Ser. B (Methodol.) **20**(2), 215–232 (1958)
5. Duman, E., Elikucuk, I.: Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization. In: Rojas, I., Joya, G., Cabestany, J. (eds.) IWANN 2013. LNCS, vol. 7903, pp. 62–71. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38682-4_8

6. Duman, E., Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst. Appl. 38(10), 13057–13063 (2011)

7. Faris, H., Aljarah, I., Mirjalili, S., Castillo, P.A., Guervós, J.J.M.: Evolopy: an open-source nature-inspired optimization framework in python. IJCCI (ECTA) 1, 171–177 (2016)

8. Gharehchopogh, F.S., Maleki, I., Dizaji, Z.A.: Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evol. Intell. 15(3), 1777–1808 (2022)

9. Jovanovic, D., Antonijevic, M., Stankovic, M., Zivkovic, M., Tanaskovic, M., Bacanin, N.: Tuning machine learning models using a group search firefly algorithm for credit card fraud detection. Mathematics 10(13), 2272 (2022)

10. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

11. Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1–5 (2017). http://jmlr.org/papers/v18/16-365.html

12. Miao, J., Niu, L.: A survey on feature selection. Procedia Comput. Sci. 91, 919–926 (2016)

13. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)

14. Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2016)

15. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

16. Murphy, K.P., et al.: Naive bayes classifiers. Univ. Br. Columbia 18(60), 1–8 (2006)

17. Obiedat, R., Harfoushi, O., Qaddoura, R., Al-Qaisi, L., Al-Zoubi, A.: An evolutionary-based sentiment analysis approach for enhancing government decisions during covid-19 pandemic: the case of Jordan. Appl. Sci. 11(19), 9080 (2021)

18. Obiedat, R., et al.: Sentiment analysis of customers’ reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution. IEEE Access 10, 22260–22273 (2022)

19. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

20. Qaddoura, R., Biltawi, M.: Improving fraud detection in an imbalanced class distribution using different oversampling techniques. In: Engineering International Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). IEEE (2022)

21. Qaddoura, R., Faris, H., Aljarah, I., Castillo, P.A.: EvoCluster: an open-source nature-inspired optimization clustering framework in python. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds.) EvoApplications 2020. LNCS, vol. 12104, pp. 20–36. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43722-0_2

22. Qaddoura, R., Faris, H., Aljarah, I., Castillo, P.A.: Evocluster: an open-source nature-inspired optimization clustering framework. SN Comput. Sci. 2(3), 1–12 (2021)
23. Sayed, G.I., Tharwat, A., Hassanien, A.E.: Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Appl. Intell. **49**(1), 188–205 (2019)

24. Sharma, S., Gosain, A., Jain, S.: A review of the oversampling techniques in class imbalance problem. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. AISC, vol. 1387, pp. 459–472. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2594-7_38

25. Taghian, S., Nadimi-Shahraki, M.H.: A binary metaheuristic algorithm for wrapper feature selection. Int. J. Comput. Sci. Eng. (IJCSE) **8**, 168–172 (2019)

26. Taghian, S., Nadimi-Shahraki, M.H., Zamani, H.: Comparative analysis of transfer function-based binary metaheuristic algorithms for feature selection. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1–6. IEEE (2018)

27. Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., Anderla, A.: Credit card fraud detection-machine learning methods. In: 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1–5. IEEE (2019)

28. Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. **14**(1), 1–37 (2008)

29. Yusta, S.C.: Different metaheuristic strategies to solve the feature selection problem. Pattern Recogn. Lett. **30**(5), 525–534 (2009)