Abstract
The reduction of the number of data instances is an important research area, particularly with a view to a reduction in the space requirements for lazy learning algorithms such as kNN. Previously, a fuzzy-rough prototype selection algorithm was proposed for this purpose, called OWAFRDC. This approach uses a criterion based on the upper and lower approximations of fuzzy-rough sets to assess the typicality of dataset instances. OWAFRDC was shown to preserve high quality instances and discard low quality instances. In this paper, a new instance quality criterion/measure is introduced to assess the quality of instances. The new criterion factors in the noisiness of instances in addition to their typicality. A numerical measure is calculated for each instance of a dataset based on the two mentioned criteria. The calculated values are used in the OWAFRDC algorithm to deliver condensed datasets. Non-parametric statistical tests show that the introduced quality measure improves the performance of OWAFRDC in terms of both accuracy and reduction rate.
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Jensen, R., Parthaláin, N.M., Amiri, M., Cassens, J. (2024). Noisy Instance Removal Using OWA-Based Fuzzy-Rough Sets. In: Panoutsos, G., Mahfouf, M., Mihaylova, L.S. (eds) Advances in Computational Intelligence Systems. UKCI 2022. Advances in Intelligent Systems and Computing, vol 1454. Springer, Cham. https://doi.org/10.1007/978-3-031-55568-8_4
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DOI: https://doi.org/10.1007/978-3-031-55568-8_4
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