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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.

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