Learning Fuzzy β-Certain and β-Possible rules from incomplete quantitative data by rough sets
This work addresses data classification challenges in noisy or incomplete datasets, but it appears incremental as it builds on existing rough-set and fuzzy set models.
The paper tackles the problem of generating fuzzy certain and possible rules from incomplete quantitative data with predefined tolerance for uncertainty and misclassification, by proposing a method that combines rough-set theory and fuzzy set theory, resulting in rules that can classify unknown objects.
The rough-set theory proposed by Pawlak, has been widely used in dealing with data classification problems. The original rough-set model is, however, quite sensitive to noisy data. Tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification. This model allowed, which combines the variable precision rough-set model and the fuzzy set theory, is thus proposed to solve this problem. This paper thus deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from incomplete quantitative data with a predefined tolerance degree of uncertainty and misclassification. A new method, incomplete quantitative data for rough-set model and the fuzzy set theory, is thus proposed to solve this problem. It first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then finding incomplete quantitative data with lower and the fuzzy upper approximations. It second calculates the fuzzy β-lower and the fuzzy β-upper approximations. The certain and possible rules are then generated based on these fuzzy approximations. These rules can then be used to classify unknown objects.