Calibrating Probability with Undersampling for Unbalanced Classification

By Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, Gianluca Bontempi. 2015

681 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Off-policy Bandits with Deficient Support (2020, methodology): “We use two realworld datasets, namely the image-classification dataset CIFAR10 [13] and the credit-card fraud dataset of [4], fromwhichwe generate various degrees of support deficient bandit data.”
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (2018, methodology): “In this section, we use a real-world Kaggle dataset (Credit card fraud detection dataset [11]) to evaluate PATE-GAN against the state-of-the-art benchmark (DPGAN [32]).”
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, methodology): “The 2013 data set has also been used in [20] and part of this data set have been suitably anonymized and made publicly available for download [22].”
Continuous-variable quantum neural networks (2018, methodology): “For the experiment, data was taken from a publicly available database of labelled historical credit card transactions which are flagged as either fraudulent or genuine [97].”
Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “CalIt2 [55] Real MTS 10,080 2 2 Urban events management CAP [168] [66] Real MTS 921,700,000 108 21 Medical and health CICIDS2017 [154] Real MTS 2,830,540 15 83 Server machines monitoring Credit Card fraud detection [44] Real MTS 284,807 1 31 Fraud detectcion DMDS [179] Real MTS 725,402 1 32 Industri”
Generative Ensembles for Robust Anomaly Detection (2018, methodology): “We consider the problem of detecting fraudulent credit card transactions from the Kaggle Credit Fraud Challenge (Dal Pozzolo et al., 2015).”
Machine Learning Testing: Survey, Landscapes and Horizons (2019, background): “Performance-based Data Bug Detection.”
Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks (2021, background): “While classification of data featuring high class imbalance has received attention in prior research, reliability of class membership probabilities in the presence of class imbalance has been previously assessed only to a very limited extent [11], [12].”
Credit card fraud detection using machine learning techniques: A comparative analysis (2017, methodology): “The dataset is sourced from ULB Machine Learning Group and description is found in [32].”
Learning Active Learning from Data (2017, methodology): “2 Real data We now turn to real data from domains where annotating is hard because it requires special training to do it correctly: Striatum, 3D Electron Microscopy stack of rat neural tissue, the task is to detect and segment mitochondria [20, 17]; MRI, brain scans obtained from the BRATS competiti”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “2), which is based on [28], we show how sampling data used to train a model induces an artificial bias into the computed posterior probabilities, for which we show a corrective method.”
Deep Learning for Anomaly Detection: A Survey (2019, background): “Credit Card Fraud Detection Anonymized credit card transactions labeled as fraudulent or genuine Dal Pozzolo et al. [2015]”
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection (2018, methodology): “We consider the problem of detecting fraudulent credit card transactions from the Kaggle Credit Fraud Challenge (Dal Pozzolo et al., 2015).”
Ensemble learning for credit card fraud detection (2018, methodology): “However, we mimic our experiments on a public dataset [7] which has similar attributes as our original dataset –skewness and distribution.”