A Novel Cost Sensitive Imbalanced Classification Method based on New Hybrid Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
محل انتشار: ماهنامه بین المللی مهندسی، دوره: 28، شماره: 8
سال انتشار: 1394
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 591
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شناسه ملی سند علمی:
JR_IJE-28-8_008
تاریخ نمایه سازی: 15 آذر 1394
چکیده مقاله:
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, ahybrid 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 usingthe proposed cost measure. Also, to improve classification performance, the lateral tuning ofmembership 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 theliterature. Experiments are performed over 37 imbalanced datasets from KEEL dataset repository; theclassification 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
کلیدواژه ها:
Cost Sensitive Learning ، Fuzzy Clustering ، Fuzzy Rule-based Classification Systems ، Evolutionary Algorithms ، Lateral Tuning
نویسندگان
m Mahdizadeh
Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
m Eftekhari
Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran