MULTILABEL OVER-SAMPLING AND UNDER-SAMPLING WITH CLASS ALIGNMENT FOR IMBALANCED MULTILABEL TEXT CLASSIFICATION

MULTILABEL OVER-SAMPLING AND UNDER-SAMPLING WITH CLASS ALIGNMENT FOR IMBALANCED MULTILABEL TEXT CLASSIFICATION

Simultaneous multiple labelling of documents, also known as multilabel text classification, will not perform optimally if the class is highly imbalanced. Class imbalanced entails skewness in the fundamental data for distribution that leads to more difficulty in classification. Random over-sampling a...

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Bibliographic Details
Main Authors: Adil Yaseen Taha, Sabrina Tiun, Abdul Hadi Abd Rahman, Ali Sabah
Format: Article
Language:English
Published: UUM Press 2021-06-01
Series:Journal of ICT
Subjects:
data mining
multilabel text classification
class imbalanced problem
resampling method
class alignment
Online Access:http://e-journal.uum.edu.my/index.php/jict/article/view/jict2021.20.3.6

Internet

http://e-journal.uum.edu.my/index.php/jict/article/view/jict2021.20.3.6

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