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Displaying similar documents to “A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring”

Computing with words and life data

Przemysław Grzegorzewski, Olgierd Hryniewicz (2002)

International Journal of Applied Mathematics and Computer Science

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The problem of statistical inference on the mean lifetime in the presence of vague data is considered. Situations with fuzzy lifetimes and an imprecise number of failures are discussed.

Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set

Yaya Liu, Keyun Qin, Chang Rao, Mahamuda Alhaji Mahamadu (2017)

International Journal of Applied Mathematics and Computer Science

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The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a...

On classification with missing data using rough-neuro-fuzzy systems

Robert K. Nowicki (2010)

International Journal of Applied Mathematics and Computer Science

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The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

Fuzzy clustering of fuzzy data considering the shape of the membership functions using a novel representation learning technique

Alireza Khastan, Elham Eskandari (2025)

Kybernetika

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Most existing distance measures for fuzzy data do not capture differences in the shapes of the left and right tails of membership functions. As a result, they may calculate a distance of zero between fuzzy data even when these differences exist. Additionally, some distance measures cannot compute distances between fuzzy data when their membership functions differ in type. In this paper, inspired by human visual perception, we propose a fuzzy clustering method for fuzzy data using a novel...