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A comparative study of misclassification cost assignment strategies for cost-sensitive AdaBoost in imbalance data classification
Name: Parinya Punsin
Organization :
Chiang Mai University. Department of Computer Science
Email :
parinya.p@cmu.ac.th
Name: Jakramate Bootkrajang
Organization :
Chiang Mai University. Department of Computer Science
Email :
jakramate.b@cmu.ac.th
LCSH:
Pattern recognition systems
-- Mathematics
LCSH:
Machine learning
LCSH:
Algorithms
LCSH:
Bioinformatics
Abstract:
Classification of data where the number of examples in each class differs significantly is not uncommon. Imbalanced data can be found in various application domains including biomedical data analysis. The traditional supervised learning following the Empirical Risk Minimisation principle, which minimises the misclassification regardless of the types of error, often yields a classification model that generalises well only on the majority class but tends to perform poorly on the minority class. Interestingly, correct classification of the minority class is usually of greater importance. Cost-sensitive learning is one of the promising approaches to introducing inductive bias into the model for imbalanced data classification. Nonetheless, there is currently a limited body of research on how to properly set the misclassification costs in imbalanced settings. To elucidate that, in this paper, we studied three strategies for determining misclassification costs for an imbalanced dataset and incorporated such costs into a cost-sensitive AdaBoost algorithm. The experimental results based on five imbalanced biomedical testbeds suggested that the proposed distribution correction method is the most effective strategy in terms of imbalance-aware performance measures.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2026-05-01
Issued:
2026-05-01
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI) Association, Thailand. 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2022) (1570791598). Bangkok : ECTI Association
eng
©copyrights King Mongkut's University of Technology North Bangkok
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ใช้เวลา
0.02493 วินาที
Parinya Punsin
| Title | Contributor | Type |
|---|
Jakramate Bootkrajang
| Title | Contributor | Type |
|---|---|---|
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