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JCR 2016
جستجوی مقالات
پنجشنبه 4 دی 1404
International Journal of Engineering
، جلد ۲۸، شماره ۸، صفحات ۱۱۶۰-۱۱۶۸
عنوان فارسی
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عنوان انگلیسی
Proposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
چکیده انگلیسی 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, 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.
کلیدواژههای انگلیسی مقاله
نویسندگان مقاله
mahdi eftekhari |
Department of Computer Engineering, Department of Computer Engineering
mahboubeh mahdizadeh |
Department of Computer Engineering, Shahid Bahonar University
نشانی اینترنتی
http://www.ije.ir/article_72562_717cda731c566ec49449e3a5f53b85ba.pdf
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اشکال در دسترسی به فایل - ./files/site1/rds_journals/409/article-409-2062469.pdf
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