SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS
By Nitesh V. Chawla, Nathalie Japkowicz, A. Kolcz. 2004
504 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/413ebd825b63404af6acd4fad59c1079cb318add
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Citations in the corpus
- An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes (2011, background): “…usually imbalanced training data sets are produced when instances from the single class are compared with all other instances in the data set, it is well-known in the field of Machine Learning that imbalanced data sets can cause some undesirable effects in the derived classifiers [15] [40] [70].”
- Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier] (2018, background): “cessed in order to achieve a balanced dataset for classification, and algorithmic-level approaches, where the classifiers are adapted to deal with the characteristic issues of imbalanced data [3]–[6].”
- The Ethical Implications of Using Artificial Intelligence in Auditing (2020, background): “Chawla et al. (2004) explain that, as in the real world, there are cases where there are more instances of some classes than others, resulting in inaccurate assumptions around the less represented classes.”
- TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (2018, background): “Themachine learning community has studied imbalanced datasets and highlighted how training and testing ratios can influence the results of an algorithm [7, 19, 44].”
- Ensemble learning for credit card fraud detection (2018, background): “Japkowicz [11],[10], [5] discussed in detail the applicability of undersampling approach for handling the class-imbalance problem.”
- Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results (2020, background): “However, accuracy is not a favorable measurement when dealing with imbalanced datasets [21] [22] [23].”