本文選題：多粒度粗糙集 + 布爾矩陣　； 參考：《計算機應用》2017年12期

【摘要】：針對已有多粒度粗糙集粒度約簡算法效率較低的問題,提出一種多粒度粗糙集粒度約簡的高效算法(EAGRMRS)。首先,以決策信息系統(tǒng)為對象,定義決策類下近似布爾矩陣,該矩陣能夠將粒度約簡過程中過多且有重復的集合運算轉換為布爾運算,基于該矩陣給出計算決策類下近似算法和計算粒度重要度算法。然后,針對計算粒度重要度時存在冗余計算的問題,提出粒度動態(tài)增加時快速計算粒度重要度的算法,并在此基礎上,提出EAGRMRS,該算法的時間復雜度為O(|A|·|U|~2+|A|~2·|U|),其中|A|表示粒度集合大小,|U|表示決策信息系統(tǒng)中實例數(shù)。在UCI數(shù)據(jù)集上的實驗結果驗證了所提算法的有效性和高效性,并且隨著數(shù)據(jù)集的增大,EAGRMRS相較于多粒度粗糙集粒度約簡的啟發(fā)式算法(HAGSS)效率優(yōu)勢更加明顯。
[Abstract]:Aiming at the low efficiency of existing multi-granularity rough set granularity reduction algorithms, an efficient algorithm of multi-granularity rough set granularity reduction is proposed.Firstly, taking decision information system as an object, the approximate Boolean matrix under decision class is defined. The matrix can transform too many and repeated set operations into Boolean operations in the process of granularity reduction.Based on the matrix, the approximate algorithm of computing decision class and the algorithm of calculating granularity importance are given.Then, aiming at the problem of redundant calculation in computing granularity importance, a fast algorithm for calculating granularity importance is proposed when granularity increases dynamically, and on this basis,EAGRMRS is proposed. The time complexity of the algorithm is O (A U ~ (2) A ~ (2) A ~ (2) U), where A denotes the size of granularity set and U represents the number of instances in a decision information system.The experimental results on UCI datasets verify the effectiveness and efficiency of the proposed algorithm, and with the increase of the data set, the efficiency of the proposed algorithm is more obvious than that of the heuristic algorithm of multi-granularity rough set granularity reduction (HAGSS).
【作者單位】： 安徽大學計算機科學與技術學院;計算智能與信號處理教育部重點實驗室(安徽大學);
【基金】：國家自然科學基金資助項目(61402005) 安徽省自然科學基金資助項目(1308085QF114) 安徽省高等學校省級自然科學基金資助項目(KJ2013A015) 安徽大學計算智能與信號處理教育部重點實驗室課題項目~~
【分類號】：TP18
，

本文編號：1769985