Learning on the border: active learning in imbalanced data classification

By Ş. Ertekin, Jian Huang, L. Eon Bottou, C. Lee Giles. International Conference on Information and Knowledge Management, 2007

449 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/805b8935d0a5937f67a6058c9cac6c18a55029cd

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In Artificial Intelligence

Citations in the corpus

ADASYN: Adaptive synthetic sampling approach for imbalanced learning (2008, methodology): “Recently, various approaches on active learning from imbalanced data sets have been proposed in literature [1] [22] [23] [24].”
SMOTE for high-dimensional class-imbalanced data (2013, background): “Others evaluated the performance of SMOTE on large data sets, focusing on problems where the number of samples, rather than the number of variables was very large [20,21].”
Evaluation Measures for Models Assessment over Imbalanced Data Sets (2013, methodology): “= The G-mean has been used per several researcher for classifier assessment over imbalanced data set, as the publication of [26] [33] [51] [57] [52]”
Selecting critical features for data classification based on machine learning methods (2020, methodology): “The problem with SVM is to separate the two classes with a function obtained from the available training data [36, 95, 96].”
A Survey of Predictive Modeling on Imbalanced Domains (2016, background): “Several approaches for imbalanced domains based on active learning have been proposed [Ertekin et al. 2007a, 2007b; Zhu and Hovy 2007; Ertekin 2013].”
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images (2016, background): “To achieve this, the training set is dynamically updated during the learning process of a single learner, putting more emphasis on informative samples [12]–[14].”
Learning from Imbalanced Data (2009, methodology): “To solve this problem, they proposed a method to effectively select such informative instances from a random set of training populations to reduce the computational cost for large-scale imbalanced data sets [98], [99].”