An Enhanced Univariate Discretization Based on Cluster Ensembles

Kittakorn Sriwanna*, Natthakan Iam-On, Tossapon Boongoen

*Awdur cyfatebol y gwaith hwn

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


Most discretization algorithms focus on the univariate case. In general, they take into account the target class or interval-wise frequency of data. In so doing, useful information regarding natural group, hidden pattern and correlation among the attributes may be inevitably lost. In response, this paper introduces a new pruning method that exploits natural groups or clusters as an explicit constraint to traditional cut-point determination techniques. This unsupervised approach makes use of cluster ensembles to reveal similarities between data belonging to adjacent intervals. To be precise, a cut-point between a pair of highly similar or related intervals will be dropped. This pruning mechanism is coupled with three different univariate discretization algorithms, with the evaluation is conducted on 10 datasets and 3 classifier models. The results suggest that the proposed method usually achieve higher classification accuracy levels, than those of the three baseline counterparts.

Iaith wreiddiolSaesneg
TeitlIntelligent and Evolutionary Systems
Is-deitlThe 19th Asia Pacific Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings
GolygyddionKittichai Lavangnananda, Somnuk Phon-Amnuaisuk, Worrawat Engchuan, Jonathan H. Chan
CyhoeddwrSpringer Nature
Nifer y tudalennau14
ISBN (Electronig)978-3-319-27000-5
ISBN (Argraffiad)978-3-319-26999-3, 978-3-319-38743-7
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 12 Tach 2015
Cyhoeddwyd yn allanolIe

Cyfres gyhoeddiadau

Enw Proceedings in Adaptation, Learning and Optimization (PALO)
ISSN (Argraffiad)2363-6084
ISSN (Electronig)2363-6092

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