Opinion surveys are widely admitted as a valuable source of information which becomes complementary to the information extracted from data by machine learning techniques. This paper focuses on a challenging and still open problem which is related to how to handle properly the inherent uncertainty of human perceptions. Namely, we propose new ways to interpret and analyze fuzzy data coming out from a special case of survey, the so-called fuzzy rating scale-based questionnaire. This kind of questionnaire is characterized by allowing expressing human perceptions in terms of fuzzy rating scales. The proposed methods are in charge of capturing and modeling the uncertainty of the answers by varying the heights of the related fuzzy sets. These methods have been validated in two case studies: (1) a descriptive survey related to the packaging design of gin bottles; and (2) a comparative survey related to 2015 IFSA-EUSFLAT conference.
Interval-valued fuzzy sets are an extension of fuzzy sets and are helpful when there is not enough information to define a membership function. This paper studies the behavior of a construction method for an interval-valued fuzzy relation built from a fuzzy relation. The behavior of this construction method is analyzed depending on the used t-norms and t-conorms, showing that different combinations of them produce a big variation in the results. Furthermore, a hybrid construction method that considers weight functions and a smoothing procedure is also introduced. Among the different applications of this method, the detection of edges in images is one of the most challenging. Thus, the performance of the proposal in detecting image edges is tested, showing that the hybrid approach that combines weights and a smoothing procedure provides better results than the non-weighted methods.
Data protection is one of the most challenging tasks nowadays due to the huge amount of information that can be shared and crossed from different sources. Releasing microdata is a way to protect data, mainly in the economic and medical field. However, this kind of data can experience privacy attacks. This paper proposes the use of fuzzy sets as a way to improve the protection of privacy in microdata. Then, traditional definitions of k-anonymity, l-diversity and t-closeness are extended. The performance of these new approaches is checked in terms of the risk index.
Privacy issues represent a longstanding problem nowadays. Measures such as k-anonymity, l-diversity and t-closeness are among the most used ways to protect released data. This work proposes to extend these three measures when the data are protected using fuzzy sets instead of intervals or representative elements.The proposed approach is then tested using Energy Information Authority data set and different fuzzy partition methods. Results shows an improvement in protecting data when data are encoded using fuzzy sets.
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