Improving rotation forest performance for imbalanced data classification through fuzzy clustering

In this paper, fuzzy C-means clustering and Rotation Forest (RF) are combined to construct a high performance classifier for imbalanced data classification. Data samples are clustered via fuzzy clustering and then fuzzy membership function matrix is added into data samples. Therefore, clusters memberships of samples are utilized as new features that are added into the original features. After that, RF is utilized for classification where the new set of features as well as the original ones are taken into account in the feature subspacing phase. The proposed algorithm utilizes SMOTE oversampling algorithm for balancing data samples. The obtained results confirm that our proposed method outperforms the other well-known bagging algorithms.