本文選題：多粒度粗糙集 + 布爾矩陣�。� 參考：《計(jì)算機(jī)應(yīng)用》2017年12期

【摘要】：針對(duì)已有多粒度粗糙集粒度約簡(jiǎn)算法效率較低的問(wèn)題,提出一種多粒度粗糙集粒度約簡(jiǎn)的高效算法(EAGRMRS)。首先,以決策信息系統(tǒng)為對(duì)象,定義決策類下近似布爾矩陣,該矩陣能夠?qū)⒘６燃s簡(jiǎn)過(guò)程中過(guò)多且有重復(fù)的集合運(yùn)算轉(zhuǎn)換為布爾運(yùn)算,基于該矩陣給出計(jì)算決策類下近似算法和計(jì)算粒度重要度算法。然后,針對(duì)計(jì)算粒度重要度時(shí)存在冗余計(jì)算的問(wèn)題,提出粒度動(dòng)態(tài)增加時(shí)快速計(jì)算粒度重要度的算法,并在此基礎(chǔ)上,提出EAGRMRS,該算法的時(shí)間復(fù)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(shí)例數(shù)。在UCI數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果驗(yàn)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(jiǎn)的啟發(fā)式算法(HAGSS)效率優(yōu)勢(shì)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(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;計(jì)算智能與信號(hào)處理教育部重點(diǎn)實(shí)驗(yàn)室(安徽大學(xué));
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61402005) 安徽省自然科學(xué)基金資助項(xiàng)目(1308085QF114) 安徽省高等學(xué)校省級(jí)自然科學(xué)基金資助項(xiàng)目(KJ2013A015) 安徽大學(xué)計(jì)算智能與信號(hào)處理教育部重點(diǎn)實(shí)驗(yàn)室課題項(xiàng)目~~
【分類號(hào)】：TP18
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本文編號(hào)：1769985