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dc.contributor.authorTien, Thanh Nguyen
dc.contributor.authorManh, Truong Dang
dc.contributor.authorAnh, Vu Luong
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorLiang, Tiancai
dc.contributor.authorMcCall, John
dc.date.accessioned2019-07-11T00:53:39Z
dc.date.available2019-07-11T00:53:39Z
dc.date.issued2019
dc.identifier.issn0031-3203
dc.identifier.doi10.1016/j.patcog.2019.06.001
dc.identifier.urihttp://hdl.handle.net/10072/386259
dc.description.abstractWith the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherELSEVIER
dc.relation.ispartofpagefrom96
dc.relation.ispartofpageto113
dc.relation.ispartofjournalPattern Recognition
dc.relation.ispartofvolume95
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0806
dc.subject.fieldofresearchcode0906
dc.titleMulti-label classification via incremental clustering on an evolving data stream
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionPost-print
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung


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