CLASSIFICATION OF IMBALANCED DATA: A REVIEW
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Machine scores (provisional)
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The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
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- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. This paper provides a review of the classification of imbalanced data regarding: the application domains; the nature of the problem; the learning difficulties with standard classifier learning algorithms; the learning objectives and evaluation measures; the reported research solutions; and the class imbalance problem in the presence of multiple classes.
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The record
- Venue
- International Journal of Pattern Recognition and Artificial Intelligence
- Topic
- Imbalanced Data Classification Techniques
- Field
- Computer Science
- Canadian institutions
- University of WaterlooPattern Discovery Technologies (Canada)
- Funders
- —
- Keywords
- Artificial intelligenceComputer scienceClassifier (UML)Machine learningOne-class classificationClass (philosophy)Data classification
- Has abstract in OpenAlex
- yes