A Classification Method for Imbalanced Data Based on SMOTE and Fuzzy Rough Nearest Neighbor Algorithm

摘要

FRNN (Fuzzy Rough Nearest Neighbor) algorithm has exhibited good performance in classifying data with inadequate features. However, FRNN does not perform well on imbalanced data. To overcome this problem, this paper introduces a combination method. An improved SMOTE method is adopted to balance data and FRNN is applied as the classification method. Experiments show that the combination method can obtain a better result rather than classical FRNN algorithm.