Rule Extraction Using Soft-Computing Techniques with Fuzzy Linguistic Representation

應用軟式計算技術擷取具模糊口語化語意之規則

謝楠楨(Nan-Chen Hsieh) ； 魏立民(Edword Wei)

《資訊管理學報》 15卷2期 (2008/04) Pp. 225-244

https://doi.org/10.6382/JIM.200804.0011

摘要

在真實世界處理決策問題時，由於輸入資料本身即存在有不確定性，所以要做出明確的決策具有相當的困難。以模糊集合與概略集合理論為基礎，本研究提出了一個“概略－模糊混合的方法”，以從具有量化數據的診斷資料集合中，自動的產生模糊IF-THEN規則。所提出的方法包含有四個階段：輸入資料前處理使資料能呈現模糊口語化的語意、以概略集合理論找出顯著的“屬性縮減”、以資料彙總技術產生候選的IF-THEN規則、以及以真實值評估IF-THEN規則的有效性。本研究主要的貢獻，在於提出的方法所產生之IF-THEN規則具有口語化語意呈現的能力、能於診斷資料集合中找出精簡的模糊IF-THEN規則，以及具有不確定資料容忍的能力。

關鍵字

由資料庫中發掘知識 ； 模糊IF-THEN規則 ； 軟式計算 ； 模糊集合 ； 概略集合

並列摘要

Decisions for real-world problems are not always made precisely since the input data are themselves imprecise. This study presents a rough-fuzzy hybridization method to generate fuzzy if-then rules automatically from a diagnosis dataset with quantitative data values, based on fuzzy set and rough set theory. The proposed method consists of four stages: preprocessing inputs with fuzzy linguistic representation; rough set theory in finding notable reducts; candidate fuzzy if-then rules generation by data summarization, and truth evaluation the effectiveness of fuzzy if-then rules. The main contributions of the proposed method are the capability of fuzzy linguistic representation of the if-then rules, finding concise fuzzy if-then rules from diagnosis dataset, and tolerance of imprecise data.

並列關鍵字

Knowledge discovery in databases ； fuzzy if-then rules ； soft computing ； fuzzy sets ； rough sets

參考文獻

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