Naslov
Filtering noisy instances and outliers

Autori
Gamberger, Dragan ; Lavrač, Nada

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Instance Selection and Construction for Data Mining

Urednik/ci
Liu, Huan. ; Motoda, Hiroshi.

Izdavač
Kluwer Academic Publishers

Grad
Boston (MA)

Godina
2001

Raspon stranica
375-394

ISBN
0-7923-7209-3

Ključne riječi
noise, outliers, saturation filter

Sažetak
Instance selection methods are aimed at finding a representative data subset that can replace the original dataset but still provide enough information to solve a given data mining task. If instance selection is done by sampling, the sample should preferably exclude noisy instances and outliers. This chapter presents methods for noise and outlier detection that can be incorporated into sampling as filters for data cleaning. The chapter presents the following filtering algorithms: a saturation filter, a classification filter, a combined classification-saturation filter, and a consensus saturation filter. The distinguishing feature of the novel consensus saturation filter is its high reliability which is due to the multiple detection of outliers and/or noisy instances. Medical evaluation in the problem of coronary artery disease diagnosis shows that the detected instances are indeed noisy or non-typical class representatives.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

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