Results 21 to 30 of about 23,776,838 (246)
A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems
, 2011 This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier ...
Xia Hong +8 more
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Boosting methods for multi‑class imbalanced data classification
, 2021 Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets.
Abdi, Y (via Mendeley Data)
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Semantic concept detection in imbalanced datasets based on different under-sampling strategies [PDF]
, 2011 Semantic concept detection is a very useful technique for developing powerful retrieval or filtering systems for multimedia data. To date, the methods for concept detection have been converging on generic classification schemes.
Guo, Jinlin +7 more
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SN Applied Sciences, 2021 Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi ...
Albert Buabeng +3 more
doaj +1 more source
A Novel Imbalanced Ensemble Learning in Software Defect Predication
IEEE Access, 2021 With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the
Jianming Zheng +4 more
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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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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
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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 ...
Titterington, D.M., Xue, J.H.
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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
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Imbalanced Learning Based on Data-Partition and SMOTE
Information, 2018 Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method
Huaping Guo, Jun Zhou, Chang-An Wu
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