Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data...

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Bibliographic Details
Main Authors: Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Imbalanced data
cost-sensitive
general vector machine
binary ant lion optimizer
Online Access:https://ieeexplore.ieee.org/document/9064578/

Internet

https://ieeexplore.ieee.org/document/9064578/

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