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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest,  INSPEC (IET), EBSCO, CNKI.
    • E-mail ijiee@ejournal.net
Editor-in-chief

 
University of Brunei Darussalam, Brunei Darussalam   
" It is a great honor to serve as the editor-in-chief of IJIEE. I'll work together with the editorial team. Hopefully, The value of IJIEE will be well recognized among the readers in the related field."

IJIEE 2019 Vol.9(2): 43-49 ISSN: 2010-3719
DOI: 10.18178/IJIEE.2019.9.2.703

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

Pasapitch Chujai, Kedkarn Chaiyakhan, Nittaya Kerdprasop, and Kittisak Kerdprasop
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”. Then it will adjust the information imbalance by synthesizing the data which selects nearest neighbors with Euclidean distance techniques. For the amount of data to be synthesized of each data set, it will be selected from the number of nearest neighbors that are in the opposite group. The features of the new synthesized data, it depends on the characteristics of the original data and the nearest neighbors. Then combine the existing imbalanced data sets and synthetic data sets to construct the learning model. The standard algorithm, Support Vector Machine (SVM) with polynomial kernel function, will be selected to learn these data sets. Some parameters are adjusted so that the algorithm is suitable for learning each data set. The results show that the OVO-SynDN model has satisfactory performance and reliability with high MAvA and MFA values. In addition, the OVO-SynDN model can still classify imbalanced data better than the four techniques that are compared. That means that the proposed method can be applied to the classification of unbalanced data that has more than two classes.

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

P. Chujai is with the Electrical Technology Education Department, Faculty of Industrial Education and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand (e-mail: pasapitchchujai@gmail.com).
K. Chaiyakhan with Computer Engineering Department, Rajamangala University of Technology Isan, Muang, Nakhon Ratchasima, Thailand (e-mail: kedkarnc@hotmail.com).
N. Kerdprasop and K. Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand (e-mail: nittaya.k@gmail.com, kittisakthailand@gmail.com).

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Cite:Pasapitch Chujai, Kedkarn Chaiyakhan, Nittaya Kerdprasop, and Kittisak Kerdprasop, "The One-Versus-One Classification Technique Based on Data Synthesis with Appropriate Distant Neighbors," International Journal of Information and Electronics Engineering vol. 9, no. 2, pp. 43-49, 2019.

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