DOI . ORG {
}
We began analyzing https://dl.acm.org/doi/10.1145/2907070, but it redirected us to https://dl.acm.org/doi/10.1145/2907070. The analysis below is for the second page.
Title[redir]:
A Survey of Predictive Modeling on Imbalanced Domains | ACM Computing Surveys
Description:
Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are ...
Title[redir]:
A Survey of Predictive Modeling on Imbalanced Domains | ACM Computing Surveys
Description:
Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are ...
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Keywords {🔍}
google, scholar, digital, library, imbalanced, data, learning, ieee, classification, springer, class, international, crossref, acm, conference, imbalance, mining, machine, josé, information, datasets, knowledge, vol, discovery, performance, proceedings, syst, sets, applications, regression, intelligence, japkowicz, oversampling, problem, nathalie, pattern, based, neural, francisco, approach, undersampling, analysis, publication, trans, herrera, method, costsensitive, advances, artificial, garcía,
Topics {✒️}
rare cases comprehensive nuclear-test-ban treaty 11th european conf carmen paz suárez-araujo acm digital library real-life domains real-world learning problem imbalanced domains authors real-life data suffer classify rare classes zhi-hua zhou square error back-propagation development density-based synthetic minority josé-francisco díez-pastor supervised learning problems improved svm-km model learning system behavior projection-based ensemble learning making classifiers cost-sensitive safe-level-synthetic minority error back-propagation algorithm feature selection metrics improved p-svm method imbalanced multi-instance learning josé hernández-orallo imbalanced multi-instance datasets utility-based data mining multi-class imbalance problem cost-sensitive boosting algorithms multi-class imbalance learning xue-wen chen diversified sensitivity-based undersampling support vector machines highly imbalanced data-sets high imbalanced data-sets jason van hulse theoretical aspects abstract ensemble modelsinternational journal support vector machine cost-sensitive regression facebook acm dl performance metrics aggregated classification performance utility-based regression digital text categorization vicente garcía jiménez cost-sensitive learning xu-ying liu annual international conference
Questions {❓}
- One-class versus binary classification: Which and when?
- When is undersampling effective in unbalanced classification tasks?
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