The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

By I-Cheng Yeh, Che-hui Lien. 2009

1,019 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems, Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

A Communication Efficient Vertical Federated Learning Framework (2019, background): “Default-Credit.”
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (2019, methodology): “We evaluate our approach on 3 datasets: (A) The Default dataset of Taiwanese credit card users (Dheeru and Karra Taniskidou 2017; Yeh and Lien 2009) with |N| = 30 , 000 and d = 23 features, where we predict whether individuals will default and the protected attribute is gender; (B) The Adult…”
The Price of Fair PCA: One Extra Dimension (2018, background): “The ﬁrst one is labeled faces in the wild (LFW) [Huang et al., 2007], the second is the Default Credit data set [Yeh and Lien, 2009].”
A Convex Framework for Fair Regression (2017, methodology): “It has been noted [32] that linear models tend to perform quite poorly on this task.”
Actionable Recourse in Linear Classification (2018, neutral): “We consider a processed version of credit dataset [41].”
Algorithmic Fairness (2020, methodology): “This dataset was used, for example, by [13] and [141], where gender was studied as the sensitive feature.”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “Most commonly, the Adult [3], Australian Credit [58], German Credit [17], GiveMeCredit [187], COMPAS [100], HELOC [83], etc.”
The random forest algorithm for statistical learning (2020, methodology): “2 The random forest algorithm We ﬁrst tree-based models because they form the building blocks of the random forest algorithm.”
A survey on datasets for fairness‐aware machine learning (2021, background): “…and other organizations to improve their operational quality and assist in decision-making in areas such as loan approval (Mukerjee, Biswas, Deb, & Mathur, 2002), re-cruiting (Faliagka, Ramantas, Tsakalidis, & Tzimas, 2012), school admission (Moore, 1998), risk prediction (Yeh & Lien, 2009).”
Intelligent financial fraud detection: A comprehensive review (2016, neutral): “Advancements in modern technologies such as the internet and mobile computing have led to an increase in financial fraud in recent years [54].”
Tilted Empirical Risk Minimization (2020, background): “In Figure 5, we plot the aggregate loss for two groups (high vs. low education) in the Default Credit dataset [70] for diﬀerent target dimensions r .”
Measuring Bias (2023, background): “The protected group in this dataset is gender [41].”
Does mitigating ML's impact disparity require treatment disparity? (2017, background): “Income UCI [22] Gender (female) income > $50k 32,561 Marketing UCI [23] Status (married) customer subscribes 45,211 Credit UCI [24] Gender (female) credit card default 30,000 Employee Attr.”
A Review on Fairness in Machine Learning (2022, methodology): “This dataset was used, for example, by Bechavod and Ligett [17] and Yeh and Lien [213], where gender was studied as the sensitive feature.”
Model-Agnostic Counterfactual Explanations for Consequential Decisions (2019, methodology): “In this section, we empirically evaluate MACE at generating counterfactual explanations on three real-world datasets in the context of loan approval (Adult [1] and Credit [34] datasets) and pretrial bail (COMPAS dataset [18]).”
Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “Dataset Data Type Size Description Income [157] Tabular 48842 Demographics and income features Bank [158] Tabular 41188 Demographics and economic features Credit Card [159] Tabular 30000 Demographics and payments Give Me Some Credit [160] Tabular 250000 Debt features MIMIC III [161] Tabular 42276 Me”
Fair Algorithms for Clustering (2019, background): “(3) creditcard [42] with 30,000 points, related to information on credit card holders from a certain credit card in Taiwan.”