Minority report in fraud detection: classification of skewed data

By Clifton Phua, Damminda Alahakoon, Vincent Cheng-Siong Lee. SKDD, 2004

660 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c9223210e27ba52b02d81441eb54102a7e31eda5

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In Artificial Intelligence

Citations in the corpus

Credit Card Fraud Detection Using Hidden Markov Model (2012, background): “Phua et al. [15] suggest the use of metaclassifier similar to [6]infrauddetectionproblems.”
Outlier Analysis (2013, methodology): “Different methods for fraud detection in the context of mobile phone networks are discussed in [225, 350, 367, 368, 444].”
Anomaly detection: A survey (2009, neutral): “Issues that arise due to imbalanced class distributions have been addressed in the data mining and machine learning literature [Joshi et al. 2001; 2002; Chawla et al. 2004; Phua et al. 2004; Weiss and Hirsh 1998; Vilalta and Ma 2002].”
Hidden Markov Model (2010, neutral): “[15] suggest the use of metaclassifier similar to [6] in fraud detection problems.”
A systematic study of the class imbalance problem in convolutional neural networks (2017, background): “They change the class distribution of a training set [33] that is most often, but not necessarily, made uniform.”
Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, methodology): “Many statistic and machine learning techniques have been developed for tackling fraud [54], for example, Neural Network, Decision Tree [48], Logistic Regression [4] and Rule-based Expert Systems [22].”
Editorial: special issue on learning from imbalanced data sets (2004, background): “al [42] combine bagging and stacking to identify the best mix of classifiers.”
Data Mining for Imbalanced Datasets: An Overview (2005, background): “Phua et. a1 (Phua and Alahakoon, 2004) combine bagging and stacking to identify the best mix of classifiers.”
On the Class Imbalance Problem (2008, background): “[49] combined bagging and stacking to identify the best mix of classifiers.”
Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms (2011, positive): “At a low evaluation relative error cost and a high evaluation prior fraud probability, stacking and C4.5 perform signiﬁcantly worse than all the other classiﬁcation algorithms.”
Graph based anomaly detection and description: a survey (2014, methodology): “…card fraud [Bolton and Hand, 2001], calling card and telecommunications fraud [Cortes et al., 2002, Taniguchi et al., 1998], auto insurance fraud [Phua et al., 2004], health insurance claim errors [Kumar et al., 2010], accounting inefficiencies [McGlohon et al., 2009], email and Web spam…”
Data mining with imbalanced class distributions: concepts and methods (2009, background): “Some examples are credit card fraud detection, where the number of fraudulent operations is much lower than the number of non-fraudulent ones [1]; rare disease medical findings, where the number of patients having the disease is very low in the population [2]; and continuous fault-monitoring tasks w”
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers (2018, background): “Traditionally, based on the availability of the data labels, OOD detection methods can be categorized into supervised [16], semi-supervised [4] and unsupervised methods [15], [3].”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, methodology): “Consequently, stacking has been effectively utilized in related studies on insurance and credit card fraud detection, outperforming the use of individual classifiers (Chan et al. 1999; Phua et al. 2004).”
Credit card fraud detection using machine learning techniques: A comparative analysis (2017, methodology): “5 algorithms are applied to skewed data partitions derived from minority oversampling with replacement [25].”
Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “In this paper [7] author has presented the data mining classification technique, then it applied in to the skewed data, that means it is an problem and it is fraud detection in finance data, and continuously submit the minority report in fraud detection.”