• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Online classification via self-organizing space partitioning

      Thumbnail
      View / Download
      1.8 Mb
      Author
      Ozkan, H.
      Vanli, N. D.
      Kozat, S. S.
      Date
      2016
      Source Title
      IEEE Transactions on Signal Processing
      Print ISSN
      1053-587X
      Publisher
      Institute of Electrical and Electronics Engineers Inc.
      Volume
      64
      Issue
      15
      Pages
      3895 - 3908
      Language
      English
      Type
      Article
      Item Usage Stats
      103
      views
      102
      downloads
      Abstract
      The 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.
      Keywords
      Adaptive
      Classification
      Online learning
      Randomized algorithms
      Selforganizing
      Sequential
      Tree
      Permalink
      http://hdl.handle.net/11693/36495
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TSP.2016.2557307
      Collections
      • Department of Electrical and Electronics Engineering 3524
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy