The One-Versus-One Classification Technique Based on Data Synthesis with Appropriate Distant Neighbors

Authors

  • Pasapitch Chujai, Kedkarn Chaiyakhan, Nittaya Kerdprasop, and Kittisak Kerdprasop Author

Keywords:

SVM with polynomial kernel function, synthesizing imbalance data, multiclass imbalanced datasets classification, one-versus-one, binary decomposition.

Abstract

Classification of unbalanced information is a problem of machine learning frameworks and learning of existing basic algorithms which will be more complicated if the data has more than two classes. Therefore, this research proposes a concept to improve the classification of unbalanced data with more than two classes with a model called One Versus-One Classification Technique based on Data Synthesis with Appropriate Distant Neighbors (OVO-SynDN). This OVO-SynDN model will divide the problem of classification of 
multiclass data into binary class data classification with the learning technique “One-Versus-One”. 

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Published

12.11.2024

How to Cite

The One-Versus-One Classification Technique Based on Data Synthesis with Appropriate Distant Neighbors . (2024). International Journal of Information and Electronics Engineering, 11(4), 9-15. http://ijiee.org/index.php/ijiee/article/view/776