Online classification via self-organizing space partitioning

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dc.citation.epage3908en_US
dc.citation.issueNumber15en_US
dc.citation.spage3895en_US
dc.citation.volumeNumber64en_US
dc.contributor.authorOzkan, H.en_US
dc.contributor.authorVanli, N. D.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2018-04-12T10:42:17Z
dc.date.available2018-04-12T10:42:17Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThe authors study online supervised learning under the empirical zero-one loss and introduce a novel classification algorithm with strong theoretical guarantees. The proposed method is a highly dynamical self-organizing decision tree structure, which adaptively partitions the feature space into small regions and combines (takes the union of) the local simple classification models specialized in those regions. The authors' approach sequentially and directly minimizes the cumulative loss by jointly learning the optimal feature space partitioning and the corresponding individual partition-region classifiers. They mitigate overtraining issues by using basic linear classifiers at each region while providing a superior modeling power through hierarchical and data adaptive models. The computational complexity of the introduced algorithm scales linearly with the dimensionality of the feature space and the depth of the tree. Their algorithm can be applied to any streaming data without requiring a training phase or a priori information, hence processing data on-the-fly and then discarding it. Therefore, the introduced algorithm is especially suitable for the applications requiring sequential data processing at large scales/high rates. The authors present a comprehensive experimental study in stationary and nonstationary environments. In these experiments, their algorithm is compared with the state-of-the-art methods over the well-known benchmark datasets and shown to be computationally highly superior. The proposed algorithm significantly outperforms the competing methods in the stationary settings and demonstrates remarkable adaptation capabilities to nonstationarity in the presence of drifting concepts and abrupt/sudden concept changes. © 1991-2012 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:42:17Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/TSP.2016.2557307en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/36495
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2016.2557307en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectAdaptiveen_US
dc.subjectClassificationen_US
dc.subjectOnline learningen_US
dc.subjectRandomized algorithmsen_US
dc.subjectSelforganizingen_US
dc.subjectSequentialen_US
dc.subjectTreeen_US
dc.titleOnline classification via self-organizing space partitioningen_US
dc.typeArticleen_US

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