LW-ELM: A Fast and Flexible Cost-Sensitive Learning Framework for Classifying Imbalanced Data

LW-ELM: A Fast and Flexible Cost-Sensitive Learning Framework for Classifying Imbalanced Data

Learning from imbalanced data is a challenging task in the fields of machine learning and data mining. As an effective and efficient solution, cost-sensitive learning has been widely adopted to address class imbalance learning (CIL) problems. Weighted extreme learning machine (WELM), which is constr...

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Bibliographic Details
Main Authors: Hualong Yu, Changyin Sun, Xibei Yang, Shang Zheng, Qi Wang, Xiaoyan Xi
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Class imbalance learning
cost-sensitive learning
extreme learning machine
weighted extreme learning machine
multi-label learning
Online Access:https://ieeexplore.ieee.org/document/8361791/

Internet

https://ieeexplore.ieee.org/document/8361791/

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