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Conference Paper: A balanced ensemble approach to weighting classifiers for text classification

TitleA balanced ensemble approach to weighting classifiers for text classification
Authors
Issue Date2007
Citation
Proceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 869-873 How to Cite?
AbstractThis paper studies the problem of constructing an effective heterogeneous ensemble classifier for text classification. One major challenge of this problem is to formulate a good combination function, which combines the decisions of the individual classifiers in the ensemble. We show that the classification performance is affected by three weight components and they should be included in deriving an effective combination function. They are: (1) Global effectiveness, which measures the effectiveness of a member classifier in classifying a set of unseen documents; (2) Local effectiveness, which measures the effectiveness of a member classifier in classifying the particular domain of an unseen document; and (3) Decision confidence, which describes how confident a classifier is when making a decision when classifying a specific unseen document. We propose a new balanced combination function, called Dynamic Classifier Weighting (DCW), that incorporates the aforementioned three components. The empirical study demonstrates that the new combination function is highly effective for text classification. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/93285
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorFung, GPCen_HK
dc.contributor.authorYu, JXen_HK
dc.contributor.authorWang, Hen_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorLiu, Hen_HK
dc.date.accessioned2010-09-25T14:56:29Z-
dc.date.available2010-09-25T14:56:29Z-
dc.date.issued2007en_HK
dc.identifier.citationProceedings - Ieee International Conference On Data Mining, Icdm, 2007, p. 869-873en_HK
dc.identifier.issn1550-4786en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93285-
dc.description.abstractThis paper studies the problem of constructing an effective heterogeneous ensemble classifier for text classification. One major challenge of this problem is to formulate a good combination function, which combines the decisions of the individual classifiers in the ensemble. We show that the classification performance is affected by three weight components and they should be included in deriving an effective combination function. They are: (1) Global effectiveness, which measures the effectiveness of a member classifier in classifying a set of unseen documents; (2) Local effectiveness, which measures the effectiveness of a member classifier in classifying the particular domain of an unseen document; and (3) Decision confidence, which describes how confident a classifier is when making a decision when classifying a specific unseen document. We propose a new balanced combination function, called Dynamic Classifier Weighting (DCW), that incorporates the aforementioned three components. The empirical study demonstrates that the new combination function is highly effective for text classification. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - IEEE International Conference on Data Mining, ICDMen_HK
dc.titleA balanced ensemble approach to weighting classifiers for text classificationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICDM.2006.2en_HK
dc.identifier.scopuseid_2-s2.0-84878065641en_HK
dc.identifier.hkuros135464en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34748846281&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage869en_HK
dc.identifier.epage873en_HK
dc.identifier.scopusauthoridFung, GPC=9732531300en_HK
dc.identifier.scopusauthoridYu, JX=7405530530en_HK
dc.identifier.scopusauthoridWang, H=9733957100en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridLiu, H=7409747220en_HK

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