MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data

Takahiro Komamizu; Risa Uehara; Yasuhiro Ogawa; Katsuhiko Toyama

doi:10.1007/978-3-030-59051-2_14

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MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data

Takahiro Komamizu Risa Uehara Yasuhiro Ogawa Katsuhiko Toyama

Year: 2020 Lecture notes in computer science Pages: 213-228 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-59051-2_14

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Keywords:

Undersampling Weighting Oversampling Computer science Gaussian Sampling (signal processing) Function (biology) Machine learning Gaussian process Artificial intelligence Pattern recognition (psychology) Bandwidth (computing)

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Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data

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