The One-Versus-One Classification Technique Based on Data Synthesis with Appropriate Distant Neighbors
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”. 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.
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
