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dc.contributor.authorZhang, X
dc.contributor.authorYao, L
dc.contributor.authorHuang, C
dc.contributor.authorWang, S
dc.contributor.authorTan, M
dc.contributor.authorLong, G
dc.contributor.authorWang, C
dc.date.accessioned2019-07-04T12:39:47Z
dc.date.available2019-07-04T12:39:47Z
dc.date.issued2018
dc.identifier.isbn9780999241127
dc.identifier.issn1045-0823
dc.identifier.doi10.24963/ijcai.2018/432
dc.identifier.urihttp://hdl.handle.net/10072/381387
dc.description.abstractMultimodel wearable sensor data classificationplays an important role in ubiquitous computingand has a wide range of applications in variousscenarios from healthcare to entertainment. How-ever, most of the existing work in this field em-ploys domain-specific approaches and is thus inef-fective in complex situations where multi-modalitysensor data is collected. Moreover, the wearablesensor data is less informative than the conven-tional data such as texts or images. In this paper,to improve the adaptability of such classificationmethods across different application contexts, weturn this classification task into a game and applya deep reinforcement learning scheme to dynami-cally deal with complex situations. We also intro-duce a selective attention mechanism into the rein-forcement learning scheme to focus on the crucialdimensions of the data. This mechanism helps tocapture extra information from the signal, and canthus significantly improve the discriminative powerof the classifier. We carry out several experimentson three wearable sensor datasets, and demonstratecompetitive performance of the proposed approachcompared to several state-of-the-art baselines.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInternational Joint Conferences on Artificial Intelligence
dc.relation.ispartofchapter43623
dc.relation.ispartofconferencenameIJCAI-18
dc.relation.ispartofconferencetitleIJCAI International Joint Conference on Artificial Intelligence
dc.relation.ispartofdatefrom2018-07-13
dc.relation.ispartofdateto2018-07-19
dc.relation.ispartoflocationStockholm, Sweden
dc.relation.ispartofpagefrom3111
dc.relation.ispartofpageto3117
dc.relation.ispartofvolume2018-July
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleMulti-modality Sensor Data Classification with Selective Attention
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2018 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorWang, Sen
gro.griffith.authorWang, Can


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