https://openalex.org/T11652
This cluster of papers focuses on the challenges and techniques for handling imbalanced data in classification problems. It covers methods such as SMOTE, ROC analysis, cost-sensitive learning, ensemble methods, and their applications in fraud detection. The cluster also discusses the use of precision-recall and boosting algorithms, as well as the effectiveness of random forest in addressing imbalanced datasets.
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rdfs:label "Handling Imbalanced Data in Classification Problems"@en ;
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skos:definition "This cluster of papers focuses on the challenges and techniques for handling imbalanced data in classification problems. It covers methods such as SMOTE, ROC analysis, cost-sensitive learning, ensemble methods, and their applications in fraud detection. The cluster also discusses the use of precision-recall and boosting algorithms, as well as the effectiveness of random forest in addressing imbalanced datasets."@en ;
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skos:prefLabel "Handling Imbalanced Data in Classification Problems"@en ;
openalex:cited_by_count 441675 ;
openalex:works_count 19713 .