Results 31 to 40 of about 219,349 (181)
Imbalanced Data Classification Algorithm Based on CSD-ELM [PDF]
Jisuanji gongcheng, 2019 The Extreme Learning Machine(ELM) based on cost-sensitive learning has its advantages in dealing with imbalanced data classification problems.However,it fails to consider the distribution characteristics of samples in different classes and the importance
WANG Dafei, XIE Wujie, DONG Wenhan
doaj +1 more source
TGT: A Novel Adversarial Guided Oversampling Technique for Handling Imbalanced Datasets
Egyptian Informatics Journal, 2021 With the volume of data increasing exponentially, there is a growing interest in helping people to benefit from their data regardless of its poor quality.
Ayat Mahmoud +3 more
doaj +1 more source
Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data
Sensors, 2019 The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets.
Kewen Li +4 more
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Do unbalanced data have a negative effect on LDA? [PDF]
, 2008 For two-class discrimination, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557–562] claimed that, when covariance matrices of the two classes were unequal, a (class ...
Anderson +12 more
core +1 more source
SMOTE-LOF for noise identification in imbalanced data classification
Journal of King Saud University: Computer and Information Sciences, 2022 Imbalanced data typically refers to a condition in which several data samples in a certain problem is not equally distributed, thereby leading to the underrepresentation of one or more classes in the dataset.
Asniar +2 more
doaj +1 more source
Multi-class Boosting for Imbalanced Data [PDF]
, 2015 We consider the problem of multi-class classification with imbalanced data-sets. To this end, we introduce a cost-sensitive multi-class Boosting algorithm (BAdaCost) based on a generalization of the Boosting margin, termed multi-class cost-sensitive margin.
Fernández Baldera, Antonio +2 more
openaire +2 more sources
Severely imbalanced Big Data challenges: investigating data sampling approaches
Journal of Big Data, 2019 Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class.
Tawfiq Hasanin +3 more
doaj +1 more source
Oversampling Algorithm Oriented to Subdivision of Minority Class in Imbalanced Data Set [PDF]
Jisuanji gongcheng, 2017 The distributions of the minority class samples in the imbalanced data set are discrepant.Traditional oversampling algorithms do not dispose this discrepancy.In order to handle this discrepancy,this paper proposes an oversampling algorithm oriented to ...
GU Ping,YANG Yang
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Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets [PDF]
, 2016 Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections.
Bettinger, Franck +6 more
core +2 more sources
Distribution-sensitive learning for imbalanced datasets [PDF]
, 2013 Many real-world face and gesture datasets are by nature imbalanced across classes. Conventional statistical learning models (e.g., SVM, HMM, CRY), however, are sensitive to imbalanced datasets.
Davis, Randall +2 more
core +1 more source