A Novel Cost Sensitive Imbalanced Classification Method based on New Hybrid Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms

In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This hybrid algorithm finds difficult minority instances; then, their misclassification cost will be calculated using the proposed cost measure. Also, to improve classification performance, the lateral tuning of membership functions (in data base) is employed by means of a genetic algorithm. The performance of the proposed method is compared with some cost-sensitive classification approaches taken from the literature. Experiments are performed over 22 highly imbalanced datasets from KEEL dataset repository; the classification results are evaluated using the Area Under the Curve (AUC) as a performance measure. Some statistical non-parametric tests are used to compare the classification performance of different methods in different datasets. Results reveal that our hybrid cost-sensitive fuzzy rule-based classifier outperforms other methods in terms of classification accuracy.