A case study on classification reliability

Dai, Honghua 2008, A case study on classification reliability, in ICDM Workshops 2008 : Proceedings of IEEE International Conference on Data Mining Workshops, IEEE, Piscataway, N.J., pp. 69-73.

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Title A case study on classification reliability
Author(s) Dai, Honghua
Conference name IEEE International Conference on Data Mining Workshops (2008 : Pisa, Italy)
Conference location Pisa, Italy
Conference dates 15-19 December 2008
Title of proceedings ICDM Workshops 2008 : Proceedings of IEEE International Conference on Data Mining Workshops
Editor(s) Bonchi, Francesco
Berendt, Bettina
Giannotti, Fosca
Gunopulos, Dimitrios
Turini, Franco
Zaniolo, Carlo
Ramakrishnan, Naren
Wu, Xindong
Publication date 2008
Conference series International Conference on Data Mining
Start page 69
End page 73
Publisher IEEE
Place of publication Piscataway, N.J.
Summary The reliability of an induced classifier can be affected by several factors including the data oriented factors and the algorithm oriented factors. In some cases, the reliability could also be affected by knowledge oriented factors. In this paper, we analyze three special cases to examine the reliability of the discovered knowledge. Our case study results show that (1) in the cases of mining from low quality data, rough classification approach is more reliable than exact approach which in general tolerate to low quality data; (2) Without sufficient large size of the data, the reliability of the discovered knowledge will be decreased accordingly; (3) The reliability of point learning approach could easily be misled by noisy data. It will in most cases generate an unreliable interval and thus affect the reliability of the discovered knowledge. It is also reveals that the inexact field is a good learning strategy that could model the potentials and to improve the discovery reliability.
ISBN 9780769535036
Language eng
Field of Research 080110 Simulation and Modelling
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018325

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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