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
کلمات کلیدی
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
نویسندگان
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