Handling imbalanced datasets: A review

By S. Kotsiantis, Dimitris N. Kanellopoulos, P. Pintelas. 2006

1,061 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/95dfdc02010b9c390878729f459893c2a5c0898f

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Citations in the corpus

Gene Ontology: Pitfalls, Biases, and Remedies. (2016, unknown): “with training of machine learning algorithms (31).”
Activity recognition in beach volleyball using a Deep Convolutional Neural Network (2017, background): “Class imbalance can deteriorate the performance of classifiers and distort classification performance metrics (Kotsiantis et al. 2006).”
Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning (2019, unknown): “We use the over-sampling approach to construct the balanced sample (Kotsiantis et al. 2006).”
Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, methodology): “SMOTE operates by interpolating between several minority classes examples that lie together, using the concept of k-nearest neighbor to avoid over fitting causing the decision boundaries for the minority class to spread further into the majority class space [58].”
Classification with class imbalance problem: A review (2015, background): “Studies on class imbalance classification has grown more emphasis only in recent years [1].”
Semi-automated screening of biomedical citations for systematic reviews (2010, background): “Many techniques have been proposed to mitigate the effects of class imbalance and are reviewed at length elsewhere [29,30].”
Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier (2015, background): “K-Nearest neighbour based credit card fraud detection techniques require a distance or similarity measure defined between two data instances [17][1].”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, unknown): “A review on handling imbalanced datasets (Kotsiantis et al. 2006)”
On the Class Imbalance Problem (2008, methodology): “Paper [1] reviewed various techniques for handling imbalance dataset problems.”
Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems (2017, background): “There have been a number of efforts that have tried to resolve this issue with training on imbalanced datasets [1], including using different forms of re-sampling [2], adjusting the decision threshold [3], or a mixture-of-experts approach which combines the results of many classifiers [4].”
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades (2015, methodology): “We acknowledge that Caltech101 is now considered an easy dataset for Computer Vision given the very advanced state of Computer Vision algorithms, but we foresee it posing a signiﬁcant challenge to the less mature ﬁeld of Neuromorphic Vision.”
Selecting Representative Data Sets (2012, background): “Two good and detailed reviews were published in [44, 57].”
Pitfalls in Machine Learning Research: Reexamining the Development Cycle (2020, unknown): “While recent discussion makes this seem as though it were a newly discovered problem, statistical bias in machine learning has been studied for decades [26].”