本文選題：粗糙集 + 屬性約簡��； 參考：《重慶郵電大學(xué)學(xué)報(自然科學(xué)版)》2016年06期

【摘要】：屬性約簡和值約簡是粗糙集理論中知識獲取的重要組成部分。通常,在知識獲取的過程中先進(jìn)行屬性約簡,然后在其基礎(chǔ)上進(jìn)行規(guī)則提取。但在實(shí)際應(yīng)用中,屬性約簡在簡化信息系統(tǒng)與提高規(guī)則提取效率的同時,原始信息系統(tǒng)中有些重要的條件屬性可能被丟棄,從而導(dǎo)致屬性約簡后對信息系統(tǒng)進(jìn)行知識獲取得到的規(guī)則其數(shù)量與簡化程度并不占優(yōu)。針對上述問題,提出一種基于粒度變化的規(guī)則獲取算法,通過屬性粒度從粗到細(xì)的變化,直接從原始信息系統(tǒng)中提取規(guī)則;采用該方法得到的規(guī)則與屬性約簡后得到的規(guī)則相比,它們的數(shù)量與平均每條規(guī)則包含的特征屬性數(shù)相對較少。最后,在理論分析的基礎(chǔ)上,通過實(shí)例驗(yàn)證了算法可行性,并通過實(shí)驗(yàn)驗(yàn)證了算法的正確性和高效性。
[Abstract]:Attribute reduction and value reduction are important parts of knowledge acquisition in rough set theory. Usually, attribute reduction is performed in the process of knowledge acquisition, and then rule extraction is carried out on the basis of attribute reduction. However, in practical application, attribute reduction can simplify information system and improve the efficiency of rule extraction, while some important conditional attributes in the original information system may be discarded. As a result, the number and simplification of the rules obtained from the knowledge acquisition of the information system after attribute reduction are not dominant. Aiming at the above problems, a rule acquisition algorithm based on granularity change is proposed, which extracts the rules directly from the original information system by changing the attribute granularity from coarse to fine. Compared with the rules obtained by attribute reduction, the number of rules obtained by this method is less than the average number of characteristic attributes contained in each rule. Finally, on the basis of theoretical analysis, the feasibility of the algorithm is verified by an example, and the correctness and efficiency of the algorithm are verified by experiments.
【作者單位】： 重慶郵電大學(xué)計(jì)算智能重慶市重點(diǎn)實(shí)驗(yàn)室;重慶郵電大學(xué)理學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61472056;61309014) 重慶郵電大學(xué)科研訓(xùn)練計(jì)劃項(xiàng)目(A2014-45)~~
【分類號】：TP181
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本文編號：1884663