A Fraud Detection Model Based on Feature Selection and Undersampling Applied to Web Payment Systems

Lima, Rafael; Pereira, Adriano C. M.

doi:10.1109/wi-iat.2015.13

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…However, among these primary studies, there is sufficient evidence that SLR studies on data preprocessing are lacking, as indicated by the fact that only 2% of the primary studies considered in this study followed SLR guidelines. This finding [136] N P N Y 1.5 [105] N N N Y 1.0 [86] N Y N Y 2.0 [137] N N N Y 1.0 [106] N P N Y 1.5 [83] N Y N Y 2.0 [138] N P N Y 1.5 [139] N P N Y 1.5 [140] N N N Y 1.0 [68] N Y N Y 2.0 [141] N P N Y 1.5 [142] N P N Y 1.5 [67] N P N Y 1.5 [90] N P N Y 1.5 [50] N P N Y 1.5 [53] N N N Y 1.0 [52] N P N Y 1.5 [47] N P N Y 1.5 [48] N N N Y 1.0 [143] N P N Y 1.5 [117] N Y N Y 2.0 [144] N N N Y 1.0 [145] N P N Y 1.5 [73] Y Y Y Y 4.0 [146] N P N Y 1.5 [147] N P N Y 1.5 [148] N N N Y 1.0 [149] N P N Y 1.5 [150] N P N Y 1.5 [60] N P N Y 1.5 [151] N N N Y 1.0 [152] N P N Y 1.5 [153] N N N Y 1.0 [154] N N N Y 1.0 [155] N N N Y 1.0 [156] N N N Y 1.0 [157] N N N Y 1.0 [158] N P N Y 1.5 [159] N N N Y 1.0 [160] N N N Y 1.0 [161] N N N Y 1.0 [162] N N N Y 1.0…”

Section: What Are the Limitations Of Current Research?mentioning

confidence: 63%

Systematic literature review of preprocessing techniques for imbalanced data

Felix

Lee

2019

IET softw.

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Data preprocessing remains an important step in machine learning studies. This is because proper preprocessing of imbalanced data can enable researchers to reduce defects as much as possible, which, in turn, may lead to the elimination of defects in existing data sets. Despite the remarkable achievements that have been accomplished in machine learning studies, systematic literature reviews of imbalanced data preprocessing techniques are lacking. Consequently, there are a limited number of systematic literature review studies on imbalanced data preprocessing. In this study, the authors assess the existing literature to identify the key issues related to data quality and handling and to provide a convenient collection of the techniques used to address these issues when performing data preprocessing. They applied a systematic literature review method involving a manual search to select articles published from January 2010 to September 2018 for review. The qualities of the existing studies were assessed using certain quality assessment criteria. Of the 118 relevant studies found, only 2% were identified as having been conducted following systematic literature review guidelines. This study, therefore, calls for more systematic literature review studies on data preprocessing to improve the quality of the data applied in machine learning studies.

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Section: What Are the Limitations Of Current Research?mentioning

confidence: 63%

Systematic literature review of preprocessing techniques for imbalanced data

Felix

Lee

2019

IET softw.

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“…Mengingat maraknya penipuan pada situs e-commerce yang dapat mengakibatkan kerugian finansial yang cukup besar, sebagai konsumen perlu adanya pengetahuan mengenai jenis penipuan yang umum terjadi dan metode pencegahan yang digunakan untuk mendeteksi penipuan agar terhindar dari berbagai kerugian. Beberapa penelitian sebelumnya hanya membahas tentang identifikasi dan metode pencegahan penipuan e-commerce ( Makarti, 2011;Chang & Chang, 2012;Syed & Shabbir, 2013;Valentin, 2013;Caldeira, Brandao, & Pereira, 2014;Leung, Lai, Chen, & Wan, 2014;Massa & Valverde, 2014;Hwang & Lai, 2015;JRana & Baria, 2015;Singh & Singh, 2015;Abdallah, Maarof, & Zainal, 2016;Beránek, Nýdl, & Remeš, 2016;Gerlach, Pavlovic, & Gerlach, 2016;Lima & Pereira, 2016;Yang et al, 2016;Ramadhan & Amelia, 2016;Sun et al, 2017;Prisha, Neo, Ong, & Teo, 2017;Raghava-Raju, 2017;Shaji & Panchal, 2017;Wiralestari, 2017;Renjith, 2018;Weng et al, 2018;Zhao et al, 2018;Zheng et al, 2018); Amasiatu Amiruddin et al, 2019;Carta et al, 2019;Raghavan & Gayar, 2019;Shah et al, 2019;Soomro et al, 2019. Sementara penelitian lainnya lebih fokus pada penipuan sistem pembayaran dan penipuan terkait dengan pelanggan (Keraf & Hidup, 2010;Rofiq & Mula, 2010;Raj & Portia, 2011;Hu, Liu, & Sambamurthy, 2011;…”

Section: Pendahuluanunclassified

Pemetaan Bentuk Dan Pencegahan Penipuan E-Commerce

Dewi

Mahyuni

2020

EEB

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Seiring berkembangnya transaksi jual beli, penipuan elektronik juga turut meningkat sehingga mengakibatkan banyak konsumen yang telah mengalami kerugian akibat penipuan yang terjadi. Tujuan dari penelitian ini adalah untuk mengetahui jenis penipuan yang dapat terjadi dalam perdagangan elektronik dan pencegahan yang dapat dilakukan. Dalam penelitian ini disajikan review dengan metode pendekatan interpretif atas artikel terkait, dengan proses pemetaan pada artikel yang dikumpulkan melalui situs Google Cendekia, Elsevier, Springer, Taylor & Francis, dan MDPI (Multidisciplinary Digital Publishing Institute). Dari sumber tersebut, 105 artikel berhasil dikumpulkan, setelah proses seleksi artikel berdasarkan 10 tahun terahir dan kesesuaian pembahasan akhirnya diperoleh 55 artikel. Hasil penelitian ini adalah ditemukan berbagai jenis penipuan pada keempat kategori e-coomerce serta penipuan pada sistem pebayaran dan penipuan pada e-commerce yang menyangkut pelanggan. Metode modern pendeteksi penipuan juga disajikan dalam penelitian ini, seperti data mining, jaringan bayesan, algoritma, mesin pendukung vector, pemrograman genetik, pohon pengambilan keputusan, Adaptive Neuro-Fuzzy Inference System, Situs Web Bantuan Perlindungan (PAW), dan Model Privacy Antecedent-Privacy Concern-Outcomes (APCO). Dengan penjabaran pada hasil penelitian ini, konsumen diharapkan untuk lebih berhati-hati saat melakukan transaksi di situs e-commerce agar terhindar dari berbagai tindak penipuan.

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“…Imbalanced data is perversive in all aspects of our living life, e.g., cancer gene data from hospitals for health examination [1], software defect data generated by defect detecting software [2] and telecommunication fraud data [3], [4] formed in telecommunication systems, which can be classified into different categories, the aforementioned data are called imbalanced data [5]. Generally speaking, machine learning approaches use a large amount of sample data to train…”

Section: Introductionmentioning

confidence: 99%

SOTB: Semi-Supervised Oversampling Approach Based on Trigonal Barycenter Theory

et al. 2020

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The problem of classifying imbalanced data is one of the active research directions in machine learning and bioinformatics. The imbalance of data will greatly degrade the accuracy of classifiers. Good oversampling methods can improve the diversity and validity of new samples, which cannot only solve the imbalance problem of sample data, but also greatly improve the classification accuracy. In this study, we propose the trigonal barycenter theory and a semi-supervised oversampling method, called SOTB (Semi-supervised Oversampling method based on Trigonal Barycenter theory). SOBT works to: (1) construct the non-intersecting triangles based on Mahalanobis distance; (2) combine the semi-supervised sampling method with trigonal barycenter theory to oversample the positive samples, which can cope with the data imbalance problem without affecting the quality of data. Lastly, extensive experiments were conducted to verify the effectiveness of the proposed method. The results demonstrate that SOTB can improve the validity, diversity and rationality on the distribution of the newly generated samples as well as alleviate the phenomena of over-fitting which is popular in existing oversampling approaches. In particular, when compared with the state-of-the-art oversampling methods, the results show SOTB can achieve the best classification performance.

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A Fraud Detection Model Based on Feature Selection and Undersampling Applied to Web Payment Systems

Cited by 13 publications

References 12 publications

Systematic literature review of preprocessing techniques for imbalanced data

Systematic literature review of preprocessing techniques for imbalanced data

Pemetaan Bentuk Dan Pencegahan Penipuan E-Commerce

SOTB: Semi-Supervised Oversampling Approach Based on Trigonal Barycenter Theory

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