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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Online adaptive hierarchical space partitioning classifier

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      Author(s)
      Kılıç, O. Fatih
      Vanlı, N. D.
      Özkan, Hüseyin
      Delibalta, İ.
      Kozat, Süleyman Serdar
      Date
      2016
      Source Title
      Proceedings of the IEEE 24th Signal Processing and Communications Applications Conference, SIU 2016
      Publisher
      IEEE
      Pages
      1237 - 1240
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      We introduce an on-line classification algorithm based on the hierarchical partitioning of the feature space which provides a powerful performance under the defined empirical loss. The algorithm adaptively partitions the feature space and at each region trains a different classifier. As a final classification result algorithm adaptively combines the outputs of these basic models which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.
      Keywords
      Adaptive trees
      Classification
      Computational efficiency
      On-line learning
      Permalink
      http://hdl.handle.net/11693/37701
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2016.7495970
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      • Department of Electrical and Electronics Engineering 4011
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