کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382809 660791 2014 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An enhancement for heuristic attribute reduction algorithm in rough set
ترجمه فارسی عنوان
یک پیشرفت برای الگوریتم کاهش ویژگی های اکتشافی در مجموعه خشن
کلمات کلیدی
مجموعه خشن، ابتکاری، کاهش مشخصه، تقویت کاهش ویژگی های اکتشافی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• The limitation of existing heuristic attribute reduction algorithms is analyzed.
• An enhancement for heuristic attribute reduction in rough set is proposed.
• The objective of the enhancement is to effectively achieve the optimal reduct.
• Two heuristic attribute reduction algorithms are improved with the enhancement.

Attribute reduction is one of the most important issues in the research of rough set theory. Numerous significance measure based heuristic attribute reduction algorithms have been presented to achieve the optimal reduct. However, how to handle the situation that multiple attributes have equally largest significances is still largely unknown. In this regard, an enhancement for heuristic attribute reduction (EHAR) in rough set is proposed. In some rounds of the process of adding attributes, those that have the same largest significance are not randomly selected, but build attribute combinations and compare their significances. Then the most significant combination rather than a randomly selected single attribute is added into the reduct. With the application of EHAR, two representative heuristic attribute reduction algorithms are improved. Several experiments are used to illustrate the proposed EHAR. The experimental results show that the enhanced algorithms with EHAR have a superior performance in achieving the optimal reduct.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 41, Issue 15, 1 November 2014, Pages 6748–6754
نویسندگان
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