کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406167 678064 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm
ترجمه فارسی عنوان
الگوریتم کاهش ویژگی های جدید بر اساس مجموعه ای خشن و بهبود یافته الگوریتم ماهی گیری ماهی مصنوعی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Attribute Reduction (AR) is an important preprocessing step for data mining. AR based on rough set is an efficient method. Its reduction performance has been verified to be better or comparable with other methods in large amount of works, but existing reduction algorithms have some problems such as slow convergent speed and probably converging to a local optimum. A novel attribute reduction algorithm based on Artificial Fish Swarm Algorithm (AFSA) and rough set is proposed. For AFSA has a slow convergence rate in the later phase of iterations, normal distribution function, Cauchy distribution function, multi-parent crossover operator, mutation operator and modified minimal generation gap model are adopted to improve AFSA. The attribute reduction algorithm based on improved AFSA and rough set takes full advantages of the improved AFSA and rough set,which are faster, more efficient, simpler, and easier to be implemented. Datasets in the UC Irvine (UCI) Machine Learning Repository are selected to verify the aforementioned new method. The results show that above algorithm can search the attribute reduction set effectively, and it has low time complexity and the excellent global search ability.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 174, Part A, 22 January 2016, Pages 522–529
نویسندگان
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