Abstract
This paper presents the application of optimistic and pessimistic similarity measures of interval-valued fuzzy sets (IVFS) to the problem of selecting relevant attributes as input to classification algorithms. The paper presents a modified IV-Relief algorithm using the aforementioned measures. The theoretical considerations are supported by the analysis of the effectiveness of the proposed algorithm on a well-known breast cancer diagnostic data-set. The proposed methods extend existing classification methods so that they work on uncertain data.
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Pȩkala, B., Dyczkowski, K., Szkoła, J., Kosior, D.: Classification of uncertain data with a selection of relevant features based on similarities measures of Interval-Valued Fuzzy Sets. In: IEEE International Conference on Fuzzy System, pp. 1–8 (2021)
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Pękala, B., Dyczkowski, K., Szkoła, J., Kosior, D. (2022). Selection of Relevant Features Based on Optimistic and Pessimistic Similarities Measures of Interval-Valued Fuzzy Sets. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_26
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