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

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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Bibliographic Details
Main Authors: Chih-Fong Tsai, Wei-Chao Lin
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Data mining
one-class classifiers
class imbalance
machine learning
ensemble learning
Online Access:https://ieeexplore.ieee.org/document/9326335/

Internet

https://ieeexplore.ieee.org/document/9326335/

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