Effective instance selection using the fuzzy-rough lower approximation

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

1 Dyfyniadau(SciVal)

Crynodeb

Fuzzy-rough set theory has been applied with much success to the problem of feature selection, where there is a clear link between the constructs (i.e., lower approximation, positive region, etc) and the problem (i.e., finding reducts). However, there has not been much development with regards to instance selection. Previous techniques have focused on preserving the positive region or the dependency, or have concentrated on prototype selection. This paper proposes a general instance selection approach that is efficient and effective in finding reductions that maintain the integrity of the underlying class structure. By utilizing the lower approximation information, instances can be removed that have a high similarity with more representative instances of a class. In addition to a threshold-based approach, a fully automated method that requires no user input is also presented. The experimentation demonstrates that the proposed method is indeed effective at reducing the number of instances with minimal information loss.

Iaith wreiddiolSaesneg
Teitl2019 IEEE International Conference on Fuzzy Systems
Is-deitlFUZZ-IEEE
CyhoeddwrIEEE Press
ISBN (Electronig)9781538617281
ISBN (Argraffiad)9781538617298
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 10 Hyd 2019
DigwyddiadFuzzy Systems - J W Marriott, New Orleans, Unol Daleithiau America
Hyd: 23 Meh 201926 Meh 2019
Rhif y gynhadledd: 28

Cyfres gyhoeddiadau

EnwIEEE International Conference on Fuzzy Systems
CyhoeddwrIEEE
Cyfrol2019
ISSN (Argraffiad)1098-7584
ISSN (Electronig)1558-4739

Cynhadledd

CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2019
Gwlad/TiriogaethUnol Daleithiau America
DinasNew Orleans
Cyfnod23 Meh 201926 Meh 2019

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