Feature Selection and Ensemble Learning Techniques in One-Class Classifiers: An Empirical Study of Two-Class Imbalanced Datasets

Class imbalance learning is an important research problem in data mining and machine learning. Most solutions including data levels, algorithm levels, and cost sensitive approaches are derived using multi-class classifiers, depending on the number of classes to be classified. One-class classificatio...

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Veröffentlicht in:	IEEE access 2021-01, Vol.9, p.1-1
Hauptverfasser:	Tsai, Chih-Fong, Lin, Wei-Chao
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Anomalies Anomaly detection class imbalance Classification algorithms Classifiers Data analysis Data mining Datasets Ensemble learning Feature extraction Feature selection Machine learning one-class classifiers Outliers (statistics) Performance enhancement Support vector machines Testing Training Training data Vegetation
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