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 37 imbalanced datasets from keel dataset repository; the classification results are evaluated using the area under the curve (auc) as a performance measure. results reveal that our hybrid cost-sensitive fuzzy rule-based classifier outperforms other methods in terms of classification accuracy.