Novelty detection using soft partitioning and hierarchical models

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dc.contributor.authorErgen, Tolgaen_US
dc.contributor.authorGökçesu, Kaanen_US
dc.contributor.authorŞimşek, Mustafaen_US
dc.contributor.authorKozat, Süleyman Serdaren_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2018-04-12T11:45:16Z
dc.date.available2018-04-12T11:45:16Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 15-18 May 2017en_US
dc.descriptionConference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.description.abstractIn this paper, we study novelty detection problem and introduce an online algorithm. The algorithm sequentially receives an observation, generates a decision and then updates its parameters. In the first step, to model the underlying distribution, algorithm constructs a score function. In the second step, this score function is used to make the final decision for the observed data. After thresholding procedure is applied, the final decision is made. We obtain the score using versatile and adaptive nested decision tree. We employ nested soft decision trees to partition the observation space in an hierarchical manner. Based on the sequential performance, we optimize all the components of the tree structure in an adaptive manner. Although this in time adaptation provides powerful modeling abilities, it might suffer from overfitting. To circumvent overfitting problem, we employ the intermediate nodes of tree in order to generate subtrees and we then combine them in an adaptive manner. The experiments illustrate that the introduced algorithm significantly outperforms the state of the art methods.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:45:16Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017en
dc.identifier.doi10.1109/SIU.2017.7960213en_US
dc.identifier.urihttp://hdl.handle.net/11693/37603
dc.language.isoTurkishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2017.7960213en_US
dc.source.titleProceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017en_US
dc.subjectAdaptiveen_US
dc.subjectNested treeen_US
dc.subjectNovelty detectionen_US
dc.subjectOnlineen_US
dc.subjectDecision treesen_US
dc.subjectHierarchical systemsen_US
dc.subjectTrees (mathematics)en_US
dc.subjectOver fitting problemen_US
dc.subjectState-of-the-art methodsen_US
dc.subjectThresholding proceduresen_US
dc.subjectUnderlying distributionen_US
dc.subjectSignal processingen_US
dc.titleNovelty detection using soft partitioning and hierarchical modelsen_US
dc.title.alternativeEsnek bölme ve hiyerarşik modeller kullanılarak ayrıklık sezimien_US
dc.typeConference Paperen_US

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