Noisy Instance Removal Using OWA-Based Fuzzy-Rough Sets

Richard Jensen, Neil Mac Parthalain, Mehran Amiri, Jorg Cassens

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

Crynodeb

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.
Iaith wreiddiolSaesneg
TeitlADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS, UKCI 2022
GolygyddionG Panoutsos, M Mahfouf, LS Mihaylova
Man cyhoeddiGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
CyhoeddwrSpringer Nature
Tudalennau37-48
Nifer y tudalennau12
Cyfrol1454
ISBN (Argraffiad)978-3-031-55567-1; 978-3-031-55568-8
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2024

Cyfres gyhoeddiadau

EnwAdvances in Intelligent Systems and Computing
CyhoeddwrSPRINGER INTERNATIONAL PUBLISHING AG

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