Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness

We propose a procedure devoted to the induction of a shadowed set through the post-processing of a fuzzy set, which in turn is learned from labeled data. More precisely, the fuzzy set is inferred using a modified support vector clustering algorithm, enriched in order to optimize the fuzziness grade. Finally, the fuzzy set is transformed into a shadowed set through application of an optimal alpha-cut. The procedure is tested on synthetic and real-world datasets

Statistical Approach to Fuzzy Cognitive Maps

Fuzzy cognitive maps are studied from statistical standpoint. An analogy between these maps and linear regression and logistic regression models is drawn. Practical examples are also provided.Peer reviewe

Statistical inference about the median from vague data

In traditional statistics all parameters of the mathematical model and possible observations should be well defined. Sometimes such assumption appears too rigid for the real-life problems, especially when dealing with imprecise or linguistic data. To relax this rigidity fuzzy methods are incorporated into statistics. This paper is devoted to statistical inference about the population median in the presence of vague data. We propose the notion of fuzzy median. Then we suggest a fuzzy estimator and fuzzy confidence interval for the median. Next we discuss the problem of hypothesis testing concerning the median in the presence of imprecise data. All methods presented are distribution-free

Soft methods in statistical quality control

The paper is devoted to soft methods in statistical quality control. A review of existing tools for dealing with vague data or fuzzy requirements is given. Some new procedures are also proposed