How to cite this paper
Ahmad, H., Kasasbeh, B., AL-Dabaybah, B & Rawashdeh, E. (2023). EFN-SMOTE: An effective oversampling technique for credit card fraud detection by utilizing noise filtering and fuzzy c-means clustering.International Journal of Data and Network Science, 7(3), 1025-1032.
Refrences
Ahmad, H., Kasasbeh, B., Aldabaybah, B., & Rawashdeh, E. (2023). Class balancing framework for credit card fraud de-tection based on clustering and similarity-based selection (SBS). International Journal of Information Technolo-gy, 15(1), 325-333.
Ahmad, S., Jha, S., Alam, A., Yaseen, M., & Abdeljaber, H. A. (2022). A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm. Scientific Programming, 2022.
Alkhalili, M., Qutqut, M. H., & Almasalha, F. (2021). Investigation of applying machine learning for watch-list filtering in anti-money laundering. IEEE Access, 9, 18481-18496.
Badic, B., Da-Ano, R., Poirot, K., Jaouen, V., Magnin, B., Gagnière, J., ... & Visvikis, D. (2022). Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study. European Radiology, 32(1), 405-414.
Barua, S., Islam, M. M., Yao, X., & Murase, K. (2012). MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning. IEEE Transactions on knowledge and data engineering, 26(2), 405-425.
Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29.
Bisong, E. (2019). Building machine learning and deep learning models on Google cloud platform (pp. 59-64). Berkeley, CA: Apress.
Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Advances in Knowledge Discovery and Data Min-ing: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings 13 (pp. 475-482). Springer Berlin Heidelberg.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2015, December). Calibrating probability with under-sampling for unbalanced classification. In 2015 IEEE symposium series on computational intelligence (pp. 159-166). IEEE.
Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences, 465, 1-20.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review on ensembles for the class im-balance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cy-bernetics, Part C (Applications and Reviews), 42(4), 463-484.
Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005, He-fei, China, August 23-26, 2005, Proceedings, Part I 1 (pp. 878-887). Springer Berlin Heidelberg.
Hordri, N. F., Yuhaniz, S. S., Azmi, N. F. M., & Shamsuddin, S. M. (2018). Handling class imbalance in credit card fraud using resampling methods. Int. J. Adv. Comput. Sci. Appl, 9(11), 390-396.
Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selec-tion. Journal of Big Data, 9(1), 1-17.
Japkowicz, N. (2000, June). The class imbalance problem: Significance and strategies. In Proc. of the Int’l Conf. on arti-ficial intelligence (Vol. 56, pp. 111-117).
Jiang, Y., Li, C., Sun, L., Guo, D., Zhang, Y., & Wang, W. (2021). A deep learning algorithm for multi-source data fu-sion to predict water quality of urban sewer networks. Journal of Cleaner Production, 318, 128533.
Kasasbeh, B., Aldabaybah, B., & Ahmad, H. (2022). Multilayer perceptron artificial neural networks-based model for credit card fraud detection. Indonesian Journal of Electrical Engineering and Computer Science, 26(1), 362-373.
Khader, M., Karam, M., & Fares, H. (2021). Cybersecurity Awareness Framework for Academia. Information, 12(10), 417.
Kubat, M., & Matwin, S. (1997, July). Addressing the curse of imbalanced training sets: one-sided selection. In Icml (Vol. 97, No. 1, p. 179).
Laurikkala, J. (2001). Improving identification of difficult small classes by balancing class distribution. In Artificial In-telligence in Medicine: 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 Cascais, Portu-gal, July 1–4, 2001, Proceedings 8 (pp. 63-66). Springer Berlin Heidelberg.
Lebichot, B., Le Borgne, Y. A., He-Guelton, L., Oblé, F., & Bontempi, G. (2020). Deep-learning domain adaptation tech-niques for credit cards fraud detection. In Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019 (pp. 78-88). Springer International Publishing.
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. (2018). Significantly fast and robust fuzzy c-means cluster-ing algorithm based on morphological reconstruction and membership filtering. IEEE Transactions on Fuzzy Sys-tems, 26(5), 3027-3041.
Li, H., Zou, P., Wang, X., & Xia, R. (2013). A new combination sampling method for imbalanced data. In Proceedings of 2013 Chinese Intelligent Automation Conference: Intelligent Information Processing (pp. 547-554). Springer Berlin Heidelberg.
Mishra, A., & Ghorpade, C. (2018, February). Credit card fraud detection on the skewed data using various classification and ensemble techniques. In 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-5). IEEE.
Nainggolan, R., Perangin-angin, R., Simarmata, E., & Tarigan, A. F. (2019, November). Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the Elbow method. In Journal of Physics: Conference Series (Vol. 1361, No. 1, p. 012015). IOP Publishing.
Pan, T., Zhao, J., Wu, W., & Yang, J. (2020). Learning imbalanced datasets based on SMOTE and Gaussian distribution. Information Sciences, 512, 1214-1233.
Prasetiyo, B., Muslim, M. A., & Baroroh, N. (2021, June). Evaluation performance recall and F2 score of credit card fraud detection unbalanced dataset using SMOTE oversampling technique. In Journal of Physics: Conference Se-ries (Vol. 1918, No. 4, p. 042002). IOP Publishing.
Ramentol, E., Caballero, Y., Bello, R., & Herrera, F. (2012). Smote-rs b*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory. Knowledge and information systems, 33, 245-265.
Sáez, J. A., Luengo, J., Stefanowski, J., & Herrera, F. (2015). SMOTE–IPF: Addressing the noisy and borderline exam-ples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences, 291, 184-203.
Santoso, N., Wibowo, W., & Himawati, H. (2019). Integration of synthetic minority oversampling technique for imbal-anced class. Indonesian Journal of Electrical and Engineering Computation Sciences, 13(1), 102-108.
Sharma, O. (2020). A novel activation function in convolutional neural network for image classification in deep learn-ing. In Data Science and Analytics: 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I 5 (pp. 120-130). Springer Singapore.
Tantithamthavorn, C., Hassan, A. E., & Matsumoto, K. (2018). The impact of class rebalancing techniques on the per-formance and interpretation of defect prediction models. IEEE Transactions on Software Engineering, 46(11), 1200-1219.
Torgo, L., Ribeiro, R. P., Pfahringer, B., & Branco, P. (2013). Smote for regression. In Progress in Artificial Intelli-gence: 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Angra do Heroísmo, Azores, Portugal, Sep-tember 9-12, 2013. Proceedings 16 (pp. 378-389). Springer Berlin Heidelberg.
Tran, T. C., & Dang, T. K. (2021, January). Machine learning for prediction of imbalanced data: Credit fraud detection. In 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) (pp. 1-7). IEEE.
Verbiest, N., Ramentol, E., Cornelis, C., & Herrera, F. (2014). Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection. Applied Soft Computing, 22, 511-517.
Yen, S. J., & Lee, Y. S. (2006). Under-sampling approaches for improving prediction of the minority class in an imbal-anced dataset. In Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August 16–19, 2006 (pp. 731-740). Springer Berlin Heidelberg.
Yi, X., Xu, Y., Hu, Q., Krishnamoorthy, S., Li, W., & Tang, Z. (2022). ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection. Complex & Intelligent Systems, 8(3), 2247-2272.
Zou, H. (2021, February). Analysis of best sampling strategy in credit card fraud detection using machine learning. In 2021 6th International Conference on Intelligent Information Technology (pp. 40-44).
Ahmad, S., Jha, S., Alam, A., Yaseen, M., & Abdeljaber, H. A. (2022). A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm. Scientific Programming, 2022.
Alkhalili, M., Qutqut, M. H., & Almasalha, F. (2021). Investigation of applying machine learning for watch-list filtering in anti-money laundering. IEEE Access, 9, 18481-18496.
Badic, B., Da-Ano, R., Poirot, K., Jaouen, V., Magnin, B., Gagnière, J., ... & Visvikis, D. (2022). Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study. European Radiology, 32(1), 405-414.
Barua, S., Islam, M. M., Yao, X., & Murase, K. (2012). MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning. IEEE Transactions on knowledge and data engineering, 26(2), 405-425.
Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29.
Bisong, E. (2019). Building machine learning and deep learning models on Google cloud platform (pp. 59-64). Berkeley, CA: Apress.
Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Advances in Knowledge Discovery and Data Min-ing: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings 13 (pp. 475-482). Springer Berlin Heidelberg.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2015, December). Calibrating probability with under-sampling for unbalanced classification. In 2015 IEEE symposium series on computational intelligence (pp. 159-166). IEEE.
Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences, 465, 1-20.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review on ensembles for the class im-balance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cy-bernetics, Part C (Applications and Reviews), 42(4), 463-484.
Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005, He-fei, China, August 23-26, 2005, Proceedings, Part I 1 (pp. 878-887). Springer Berlin Heidelberg.
Hordri, N. F., Yuhaniz, S. S., Azmi, N. F. M., & Shamsuddin, S. M. (2018). Handling class imbalance in credit card fraud using resampling methods. Int. J. Adv. Comput. Sci. Appl, 9(11), 390-396.
Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selec-tion. Journal of Big Data, 9(1), 1-17.
Japkowicz, N. (2000, June). The class imbalance problem: Significance and strategies. In Proc. of the Int’l Conf. on arti-ficial intelligence (Vol. 56, pp. 111-117).
Jiang, Y., Li, C., Sun, L., Guo, D., Zhang, Y., & Wang, W. (2021). A deep learning algorithm for multi-source data fu-sion to predict water quality of urban sewer networks. Journal of Cleaner Production, 318, 128533.
Kasasbeh, B., Aldabaybah, B., & Ahmad, H. (2022). Multilayer perceptron artificial neural networks-based model for credit card fraud detection. Indonesian Journal of Electrical Engineering and Computer Science, 26(1), 362-373.
Khader, M., Karam, M., & Fares, H. (2021). Cybersecurity Awareness Framework for Academia. Information, 12(10), 417.
Kubat, M., & Matwin, S. (1997, July). Addressing the curse of imbalanced training sets: one-sided selection. In Icml (Vol. 97, No. 1, p. 179).
Laurikkala, J. (2001). Improving identification of difficult small classes by balancing class distribution. In Artificial In-telligence in Medicine: 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 Cascais, Portu-gal, July 1–4, 2001, Proceedings 8 (pp. 63-66). Springer Berlin Heidelberg.
Lebichot, B., Le Borgne, Y. A., He-Guelton, L., Oblé, F., & Bontempi, G. (2020). Deep-learning domain adaptation tech-niques for credit cards fraud detection. In Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019 (pp. 78-88). Springer International Publishing.
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. (2018). Significantly fast and robust fuzzy c-means cluster-ing algorithm based on morphological reconstruction and membership filtering. IEEE Transactions on Fuzzy Sys-tems, 26(5), 3027-3041.
Li, H., Zou, P., Wang, X., & Xia, R. (2013). A new combination sampling method for imbalanced data. In Proceedings of 2013 Chinese Intelligent Automation Conference: Intelligent Information Processing (pp. 547-554). Springer Berlin Heidelberg.
Mishra, A., & Ghorpade, C. (2018, February). Credit card fraud detection on the skewed data using various classification and ensemble techniques. In 2018 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-5). IEEE.
Nainggolan, R., Perangin-angin, R., Simarmata, E., & Tarigan, A. F. (2019, November). Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the Elbow method. In Journal of Physics: Conference Series (Vol. 1361, No. 1, p. 012015). IOP Publishing.
Pan, T., Zhao, J., Wu, W., & Yang, J. (2020). Learning imbalanced datasets based on SMOTE and Gaussian distribution. Information Sciences, 512, 1214-1233.
Prasetiyo, B., Muslim, M. A., & Baroroh, N. (2021, June). Evaluation performance recall and F2 score of credit card fraud detection unbalanced dataset using SMOTE oversampling technique. In Journal of Physics: Conference Se-ries (Vol. 1918, No. 4, p. 042002). IOP Publishing.
Ramentol, E., Caballero, Y., Bello, R., & Herrera, F. (2012). Smote-rs b*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory. Knowledge and information systems, 33, 245-265.
Sáez, J. A., Luengo, J., Stefanowski, J., & Herrera, F. (2015). SMOTE–IPF: Addressing the noisy and borderline exam-ples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences, 291, 184-203.
Santoso, N., Wibowo, W., & Himawati, H. (2019). Integration of synthetic minority oversampling technique for imbal-anced class. Indonesian Journal of Electrical and Engineering Computation Sciences, 13(1), 102-108.
Sharma, O. (2020). A novel activation function in convolutional neural network for image classification in deep learn-ing. In Data Science and Analytics: 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I 5 (pp. 120-130). Springer Singapore.
Tantithamthavorn, C., Hassan, A. E., & Matsumoto, K. (2018). The impact of class rebalancing techniques on the per-formance and interpretation of defect prediction models. IEEE Transactions on Software Engineering, 46(11), 1200-1219.
Torgo, L., Ribeiro, R. P., Pfahringer, B., & Branco, P. (2013). Smote for regression. In Progress in Artificial Intelli-gence: 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Angra do Heroísmo, Azores, Portugal, Sep-tember 9-12, 2013. Proceedings 16 (pp. 378-389). Springer Berlin Heidelberg.
Tran, T. C., & Dang, T. K. (2021, January). Machine learning for prediction of imbalanced data: Credit fraud detection. In 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) (pp. 1-7). IEEE.
Verbiest, N., Ramentol, E., Cornelis, C., & Herrera, F. (2014). Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection. Applied Soft Computing, 22, 511-517.
Yen, S. J., & Lee, Y. S. (2006). Under-sampling approaches for improving prediction of the minority class in an imbal-anced dataset. In Intelligent Control and Automation: International Conference on Intelligent Computing, ICIC 2006 Kunming, China, August 16–19, 2006 (pp. 731-740). Springer Berlin Heidelberg.
Yi, X., Xu, Y., Hu, Q., Krishnamoorthy, S., Li, W., & Tang, Z. (2022). ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection. Complex & Intelligent Systems, 8(3), 2247-2272.
Zou, H. (2021, February). Analysis of best sampling strategy in credit card fraud detection using machine learning. In 2021 6th International Conference on Intelligent Information Technology (pp. 40-44).