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Compact Rule Learner on Weighted Fuzzy Approximation Spaces for Class Imbalanced and Hybrid Data
Ansvarlig organisasjon
2008 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert) Published
Abstract [en]

Rough set theory is an efficient tool for machine learning and knowledge acquisition. By introducing weightiness into a fuzzy approximation space, a new rule induction algorithm is proposed, which combines three types of uncertainty: weightiness, fuzziness and roughness. We first define the key concepts of block, minimal complex and local covering in a weighted fuzzy approximation space, then a weighted fuzzy approximation space based rule learner, and finally a weighted certainty factor for evaluating fuzzy classification rules. The time complexity of proposed rule learner is theoretically analyzed. Furthermore, in order to estimate the performance of the proposed method on class imbalanced and hybrid datasets, we compare our method with classical methods by conducting experiments on fifteen datasets. Comparative studies indicate that rule sets extracted by this method get a better performance on minority class than other approaches. It is therefore concluded that the proposed rule learner is an effective method for class imbalanced and hybrid data learning.

sted, utgiver, år, opplag, sider
Akron, OH, USA: Springer-Verlag Berlin Heidelberg , 2008.
Emneord [en]
Rule induction, fuzzy rough set, weighted rough set, hybrid attributes, class imbalanced data sets
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-8390DOI: 10.1007/978-3-540-88425-5_27Lokal ID: oai:bth.se:forskinfo9056D09372830FADC12574E3003C1E2CISBN: 978-3-540-88423-1 (tryckt)OAI: oai:DiVA.org:bth-8390DiVA, id: diva2:836105
Konferanse
6th International Conference, RSCTC 2008
Tilgjengelig fra: 2012-09-18 Laget: 2008-10-15 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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