Uncertain Distribution-Based Similarity Measure of Concepts

Abstract

The similarity of concepts is a basic task in the field of artificial intelligence, e.g., image retrieval, collaborative filtering, and public opinion guidance. As a powerful tool to express the uncertain concepts, similarity measure based on cloud model (SMCM) is always utilized to measure the similarity between two concepts. However, current studies on SMCM have two main limitations: (1) the similarity measures based on conceptual intension lack interpretability for merging the numerical characteristics and cannot discriminate some different concepts. (2) The similarity measures based on conceptual extension are always instable and inefficient. To address the above problems, an uncertain distribution-based similarity measure of cloud model (UDCM) is proposed in this paper. By analyzing the definition of the CM, we propose a new complete uncertainty including first-order and second-order uncertainty to calculate the uncertainty more accurately. Then, based on the difference between the complete uncertainty of two concepts, the computing process of UDCM and its some properties are introduced. Finally, we exhibit its advantages by comparing with other methods and verify its validity by experiments.

A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance

“…The experimental results show that MSCM has a moderate discriminative power; it also contains an integral operation process with high time complexity and its metric results are not affected by the number of cloud drops and the number of experiments. According to the results of the simulation experiments conducted by Li et al (2020) , UDCM has good discriminative ability and theoretical interpretability. However, it retains many integral operations and is less efficient.…”

“…Studies by Li, Wang & Yang (2019) and Li et al (2020) provide the evaluation metrics for cloud model similarity metrics. Table 6 shows how the EPTCM method compares with the extant methods in terms of discriminability, efficiency, stability, and interpretability.…”

“…In this algorithm, the overlapping degree is used to describe the overlapping part of two clouds, and the overlapping part is transformed into the similarity of cloud models by using the membership degree of “3 En ” boundary and the intersection of two clouds. Li et al (2020) proposed a cloud model similarity measurement method (UDCM) based on uncertain distribution. Zhang et al (2021) put forward a new similarity measurement method (MSCM) for multi-dimensional cloud models based on fuzzy similarity principle.…”