An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

By Victoria López, Alberto Fernández, Salvador Garcia, Vasile Palade, Francisco Herrera. Information Sciences, 2013

1,580 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ca9ef070d2a424b344b814de1196520da2f34ad7

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

A Survey of Predictive Modeling on Imbalanced Domains (2016, background): “L´opez et al. [2013] mentions two different perspectives of this problem under imbalanced domains: intrinsic and induced data shift.”
Deep learning in bioinformatics (2016, background): “Solutions to limited and imbalanced data can be divided into three major groups [181, 183]: data preprocessing, cost-sensitive learning and algorithmic modification.”
Learning from imbalanced data: open challenges and future directions (2016, background): “[36] and discussion on new perspectives for evaluation classifiers on skewed datasets [42] deserve mentioning.”
Big data preprocessing: methods and prospects (2016, background): “This situation is known as the class imbalance problem [43].”
Detecting Pneumatic Failures on Temporary Immersion Bioreactors (2016, methodology): “Currently, there are several classifiers designed to deal with the class imbalance problem [20–22].”
Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier] (2018, methodology): “However, the minority class is augmented without considering the structure of data: all minority examples have the same probability of being oversampled, regardless of their neighborhood, which leads to the following issues [7, 24, 40]:”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “Traditionally, imbalanced classification has been faced through techniques [6] such as resampling, cost-sensitive learning, and algorithmic-specific adaptations.”
Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases (2016, background): “Evaluation measure Although there exists several measures to evaluate the performance of the classifiers, the most used measure for class imbalance problems [10,26,30] is the AUC measure [64].”
On the use of MapReduce for imbalanced big data using Random Forest (2014, methodology): “However, there are some additional factors that negatively influence the classification with imbalanced datasets, the data intrinsic characteristics [33].”
Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, neutral): ““under-learning” problems in application [46], [47].”