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Rough Rule Extracting From Various Conditions: Incremental and Approximate Approaches for Inconsistent Data

Liu Y., Xu C., Zhang Q, Pan Y.

Fundamenta Informaticae

2008

Vol. 84, nr 3-4

403-427

Rough rule extraction refers to the rule induction method by using rough set theory. Although rough set theory is a powerful mathematical tool in dealing with vagueness and uncertainty in data sets, it is lack of effective rule extracting approach under complex conditions. This paper proposes several algorithms to perform rough rule extraction from data sets with different properties. Firstly, in order to obtain uncertainty rules from inconsistent data, we introduce the concept of confidence factor into the rule extracting process. Then, an improved incremental rule extracting algorithm is proposed based on the analysis of the incremental data categories. Finally, above algorithms are further extended to perform approximate rule extraction from huge data sets. Preliminary experiment results are encouraging.

Incomplete measurement data in the problem of classification

Adamus E.

Image Processing & Communications

2005

Vol. 10, no 2

85-92

In the article the author has attempted to supplement, the incomplete measurement samples with an introductory analysis of the profitability of the possible prediction of the missing values. In order to do that a fuzzy model, automatically tuned by a self-organising neural network, has been applied. Thus, the method may be used for solving problems connected with classification. However, the idea of treating the incomplete data has been adopted from the rough set theory.