The existing fuzzy rough set models all believe that the decision attribute divides the sample set into several “clear” decision classes, and this data processing method makes the model sensitive to noise information when conducting feature selection. To solve this problem, this paper proposes a robust fuzzy rough set model based on representative samples. Firstly, the fuzzy membership degree of the samples is defined to reflect its fuzziness and uncertainty, and RS-FRS model is constructed to reduce the influence of the (...) noise samples. RS-FRS model does not need to set parameters for the model in advance and can effectively reduce the complexity of the model and human intervention. On this basis, the related properties of RS-FRS model are studied, and the sample pair selection algorithm based on RS-FRS is used for feature selection. In this paper, RS-FRS is tested and analysed on the open 12 datasets. The experimental results show that RS-FRS model proposed can effectively select the most relevant features and has certain robustness to the noise information. The proposed model has a good applicability for data processing and can effectively improve the performance of feature selection. (shrink)

Previous studies on the Structural Alignment Model suggest that people compare the alignable attributes and nonalignable attributes during the decision-making process and preference formation process. Alignable attributes are easier to process and more effective in clue extracting. Thus, it is believed that people rely more on alignable than nonalignable attributes when comparing alternatives. This article supposes that consumers’ product experience and personal characteristics also play a significant role in regulating consumers’ reliance on attribute alignability. The authors conducted three experiments to (...) examine the moderating role of consumers’ product familiarity and self-construal in the impact of attribute alignability on consumer product purchase. The results show the following: When making a purchase decision, consumers with a high level of product familiarity will rely more on nonalignable attributes, while those with a low level of product familiarity will rely more on alignable attributes. The difference in consumer dependency on attribute alignability is driven by their perceived diagnosticity of attributes. The dependency of consumers with different levels of familiarity on attribute alignability will be further influenced by consumers’ self-construal. Individuals with interdependent self-construal rely more on alignable attributes when unfamiliar with the product, while relying more on nonalignable attributes when familiar with the product. Individuals with independent self-construal, however, rely more on nonalignable attributes regardless of the degree of product familiarity. The conclusions of this paper can be used as references for enterprises to establish product positioning and communication strategies. (shrink)