کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6858129 | 661917 | 2014 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reduction target structure-based hierarchical attribute reduction for two-category decision-theoretic rough sets
ترجمه فارسی عنوان
کاهش مجموعه ویژگی های سلسله مراتبی مبتنی بر ساختار هدف برای مجموعه های خشن مجموعه تصمیم گیری دو دسته ای
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کلمات کلیدی
نظریه مجموعه خشن، کاهش مشخصه، تصمیم گیری نظری مجموعه خشن، محاسبات گرانول، ثبات و حفظ، هدف ساختاری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Attribute reduction is an essential subject in rough set theory, but because of quantitative extension, it becomes a problem when considering probabilistic rough set (PRS) approaches. The decision-theoretic rough set (DTRS) has a threshold semantics and decision feature and thus becomes a typical and fundamental PRS. Based on reduction target structures, this paper investigates hierarchical attribute reduction for a two-category DTRS and is divided into five parts. (1) The knowledge-preservation property and reduct are explored by knowledge coarsening. (2) The consistency-preservation principle and reduct are constructed by a consistency mechanism. (3) Region preservation is analyzed, and the separability between consistency preservation and region preservation is concluded; thus, the double-preservation principle and reduct are studied. (4) Structure targets are proposed by knowledge structures, and an attribute reduction is further described and simulated; thus, general reducts are defined to preserve the structure targets or optimal measures. (5) The hierarchical relationships of the relevant four targets and reducts are analyzed, and a decision table example is provided for illustration. This study offers promotion, rationality, structure, hierarchy and generalization, and it establishes a fundamental reduction framework for two-category DTRS. The relevant results also provide some new insights into the attribute reduction problem for PRS.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 277, 1 September 2014, Pages 755-776
Journal: Information Sciences - Volume 277, 1 September 2014, Pages 755-776
نویسندگان
Xianyong Zhang, Duoqian Miao,