کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4944328 | 1437987 | 2017 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view
ترجمه فارسی عنوان
رویکرد کاهش ویژگی های افزایشی براساس جزئیات دانه بندی با یک دید چند دانه ای
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کلمات کلیدی
سیستم تصمیم گیری، یادگیری افزایشی، جزئیات دانه نظریه مجموعه خشن، کاهش مشخصه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Dynamic updating of attribute reduction is a key factor for the success of rough set theory since many real data vary dynamically with time. Though many incremental methods for updating reduct have been proposed to deal with a dynamically-varying data set and has attracted much attention. However, it is hard to update reduct when the large-scale data vary dynamically. To overcome this deficiency, in this paper, we develop an attribute reduction algorithm with a multi-granulation view to discover reduct of large-scale data sets. Then, incremental mechanisms for knowledge granularity are introduced and two corresponding incremental approaches for updating reduct are developed when many objects are varied in a large-scale decision table with a multi-granulation view. Finally, experiments have been run on six data sets from UCI and the experimental results show that the proposed incremental algorithm with a multi-granulation view can achieve better performance for large-scale data sets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 411, October 2017, Pages 23-38
Journal: Information Sciences - Volume 411, October 2017, Pages 23-38
نویسندگان
Yunge Jing, Tianrui Li, Hamido Fujita, Zeng Yu, Bin Wang,