@inproceedings{ed0db05f9ad346e89666d6e2f58ee4d1,
title = "Noisy Instance Removal Using OWA-Based Fuzzy-Rough Sets",
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.",
keywords = "Fuzzy-rough sets, prototype selection, nearest neighbour algorithm, noisy data, instance quality measure",
author = "Richard Jensen and {Mac Parthalain}, Neil and Mehran Amiri and Jorg Cassens",
note = "21st UK Workshop on Computational Intelligence (UKCI), Univ Sheffield, Sheffield, ENGLAND, SEP 07-09, 2022",
year = "2024",
doi = "10.1007/978-3-031-55568-84",
language = "English",
isbn = "978-3-031-55567-1; 978-3-031-55568-8",
volume = "1454",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Nature",
pages = "37--48",
editor = "G Panoutsos and M Mahfouf and LS Mihaylova",
booktitle = "ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2022",
address = "Switzerland",
}