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”.
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