ENHANCING ACCURACY OF IMBALACED DATASETS USING NESTED GENERALIZED EXEMPLAR LEARNING

Author (s) : B.S.Sangeetha and Dr.S.Prakashkumar

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Abstract :
In Data Mining, Classification with imbalanced datasets is considered to be a new challenge for researches in the framework of data mining. The imbalance problem occurs in many examples that represents one of the classes of the dataset is much lower than the other classes. To tackle with imbalance problem, pre-processing the datasets applied with oversampling method (SMOTE) was previously proposed. Generalized instances are belonging to the family of Nested Generalized Exemplar, which achieves storing objects in Euclidean n-space. The most representative mode used in NGE learning is: classical-BNGE and RISE, recent-INNER, rule induction-RIPPER and PART. The Fuzzy Neural Network approach, which is a combination of fuzzy logic and neural networks and called as Neuro Fuzzy System, which could improve the performance and accuracy of the existing system. The proposed approach deals with the comparison of NGE learning without using SMOTE methods.

Keywords : Nested generalized exemplar learning, Imbalanced classification, SMOTE method, Rule induction

Conference Date : 30/08/2019

Pages : 3

Paper ID : 19003