Browsing by Author "Liau, CJ"

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A logical approach to fuzzy data analysis
(1999)
Liau, CJ
;
Liu, DR
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In this paper, we investigate the extraction of fuzzy rules from data tables based on possibility theory. A possibilistic decision language is used to represent the extracted fuzzy rules. The algorithm for rule extraction is presented and the complexity analysis is carried out. Since the results of the rule induction process strongly depend on the representation language, we also discuss some approach for dynamic adjustment of the language based on the data.
A possibilistic decision logic with applications
(2001-05-01)
Liau, CJ
;
Liu, DR
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In this paper, we investigate a knowledge representation formalism in the context of fuzzy data tables. A possibilistic decision logic incorporating linguistic terms is proposed for representing and reasoning about knowledge in fuzzy data tables. Two applications based on the logic are described. The first is the extraction of fuzzy rules from general fuzzy data tables. In this application, the knowledge in the tables may be made explicit by the formulas of the logic or used implicitly in decision-making. The second is for the fuzzy quantization problem of precise data tables. It can be viewed as a special case of the first, however, due to some special properties of the problem, a polynomial time rule extraction process can be obtained. Finally, the relationship of the logic with some works for handling uncertain information in data tables is also discussed.
Justification and hypothesis selection in data mining
(2005)
Fan, TF
;
Liu, DR
;
Liau, CJ
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Data mining is an instance of the inductive methodology. Many philosophical considerations for induction can also be carried out for data mining. In particular, the justification of induction has been a long-standing problem in epistemology. This article is a recast of the problem in the context of data mining. We formulate the problem precisely in the rough set-based decision logic and discuss its implications for the research of data mining.