SMOTEBoost: Improving Prediction of the Minority Class in Boosting

By Nitesh V. Chawla, Aleksandar Lazarevic, L. Hall, Kevin W. Bowyer. 2003

1,784 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A systematic study of the class imbalance problem in convolutional neural networks (2017, background): “SMOTEBoost, on the other hand, is a combination of boosting and SMOTE oversampling [51].”
Incremental Local Outlier Detection for Data Streams (2007, neutral): “Supervised learning methods typically build a prediction model for rare events based on labeled data (the training set), and use it to classify each event [1, 2].”
A novel ensemble method for classifying imbalanced data (2015, methodology): “As most traditional classification algorithms, such as decision trees [10–12], k-nearest neighbors [13,14], and RIPPER [15,16], tend to generate models which maximize the overall classification accuracy, and the minority class is usually ignored [17–19].”
On multi-class classification through the minimization of the confusion matrix norm (2013, neutral): “NC and SmoteBoost from Wang and Yao (2012) are reported.”
Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks (2021, methodology): “Alternatively, hybrid ensemble methods that combine sampling and boosting methods [29], [30] have also been proposed in the past literature.”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “SMOTEBoost [122] combines boosting with SMOTE.”
Borderline over-sampling for imbalanced data classification (2009, methodology): “Another method is SMOTEBoost (Chawla et al., 2003), which modifies the standard AdaBoost algorithm by generating artificial instances from the minority class using SMOTE for each boosting round.”
Boosting methods for multi-class imbalanced data classification: an experimental review (2020, neutral): “SMOTEBoost supports both binary and multi-class problems [53].”
Survey of resampling techniques for improving classification performance in unbalanced datasets (2016, methodology): “Some of these include One side selection [9], ADASYN [5], SVM SMOTE [12], SMOTEBoost [2], ClusterBased Oversampling [7], Kernel-based methods and active learning [6].”
Multiclass Imbalance Problems: Analysis and Potential Solutions (2012, methodology): “NC is compared with other state-of-the-art methods in cases of using and not using class decomposition, including the conventional AdaBoost, resampling-based AdaBoost, and SMOTEBoost [4].”
Cost-sensitive decision tree ensembles for effective imbalanced classification (2014, neutral): “Dataset Single CSTree [30] MCS from [27] SMOTEBagging [8] SMOTEBoost [8] IIvotes [5] EasyEnsemble [31] Proposed SE SP SE SP SE SP SE SP SE SP SE SP SE SP Pima 59.”
On the Class Imbalance Problem (2008, neutral): “Instead of changing the distributions of training data by updating the weights associated with each example, SMOTEBoost alters the distributions by adding new examples of minority class using the SMOTE algorithm.”