Computer Information Systems and Industrial Management: 12th by Mieczysław A. Kłopotek (auth.), Khalid Saeed, Rituparna
By Mieczysław A. Kłopotek (auth.), Khalid Saeed, Rituparna Chaki, Agostino Cortesi, Sławomir Wierzchoń (eds.)
This booklet constitutes the court cases of the twelfth IFIP TC eight foreign convention, CISIM 2013, held in Cracow, Poland, in September 2013. The forty four papers offered during this quantity have been rigorously reviewed and chosen from over 60 submissions. they're equipped in topical sections on biometric and biomedical functions; development popularity and photo processing; numerous features of machine safeguard, networking, algorithms, and commercial functions. The booklet additionally includes complete papers of a keynote speech and the invited talk.
Read or Download Computer Information Systems and Industrial Management: 12th IFIP TC8 International Conference, CISIM 2013, Krakow, Poland, September 25-27, 2013. Proceedings PDF
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Extra resources for Computer Information Systems and Industrial Management: 12th IFIP TC8 International Conference, CISIM 2013, Krakow, Poland, September 25-27, 2013. Proceedings
It means that the concept drift appearances have the weakest impact on the WAE accuracy. 6 Conclusions We discussed the ensemble classiﬁer approach applied to data stream classiﬁcation task. , its line-up is formed when new data chunk is come and the decision which classiﬁer is chosen to the ensemble is made on the basis of General Diversity (diversity measure). The decision about object’s label is made according to weighted voting where weight assigned to a given classiﬁer depends on its accuracy (proportional) and how long the classiﬁer participates in the ensemble (inversely proportional).
In  Sharkley et al. proposed four level of diversity based on the majority voting rule answer, coincident error, and possibility of at least one correct answer of ensemble members. Brown et al.  reﬂected that it is not appropriate for the case where diversity of an ensemble is diﬀer in diﬀer subspace of the feature space. 16 3 M. Wo´zniak Classiﬁer Ensembles for Data Stream Among the most popular ensemble approaches, the following are worth noting: the Streaming Ensemble Algorithm (SEA)  or the Accuracy Weighted Ensemble (AWE).
402–407. Springer, Heidelberg (1993) 37. : Tracking context changes through meta-learning. Mach. Learn. 27, 259–286 (1997) Application of Combined Classiﬁers to Data Stream Classiﬁcation 23 38. : Learning recurring concepts from data streams with a context-aware ensemble. C. ) Proceedings of the 2011 ACM Symposium on Applied Computing (SAC), TaiChung, Taiwan, March 21-24, pp. 994–999. ACM (2011) 39. : Tracking recurring contexts using ensemble classiﬁers: an application to email ﬁltering. Knowl.