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To deal with set-valued data, one usually exploits the information value similarity, which is fed back to the object set. Alternatively, it is feasible but scarcely considered to handle set-valued data from the perspective of the information-value similarity which is fed back to the feature set. Based on this perspective, this paper studies feature selection for a set-valued decision information system (SVDIS) by means of fuzzy rough iterative computation model. In order to describe the similarity between objects, the fuzzy symmetric relations in an SVDIS are first defined, and a variable parameter to control the similarity is introduced. Then, new fuzzy rough set model for set valued data is proposed, and this model employs the iterative computation strategy to define fuzzy rough approximations and dependency functions. Next, fuzzy rough iterative computation model is established by using the iteration of fuzzy positive region and fuzzy dependency. Moreover, fuzzy rough iterative computation model is applied to the feature selection for set-valued data, and the corresponding algorithm is given. Finally, experiments are carried out to evaluate the performance of the given algorithm. The experimental results indicate that the given algorithm is more effective than some existing algorithms. [ABSTRACT FROM AUTHOR]

Attribute reduction can remove data noise and redundancy, thus reducing computational complexity, which is very important for machine learning. Because the difference between nominal attribute values is difficult to measure, attribute reduction for hybrid data faces challenges. In addition, most of the existing methods are sensitive to noise due to the lack of an anti-noise mechanism. Decision attribute contains the most important information of data. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a new fuzzy relation with an anti-noise mechanism is defined. Furthermore, fuzzy belief and fuzzy plausibility are defined based on the defined new distance and new fuzzy relation. In this framework, two anti-noise attribute reduction algorithms for hybrid data are proposed. Experiments on 12 data sets of various types show that compared with the other 8 state-of-the-art algorithms, the proposed algorithms improve the classification accuracy by at least 2% and the anti-noise ability by at least 11%. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability. [ABSTRACT FROM AUTHOR]