Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      CS-ARF: Compressed adaptive random forests for evolving data stream classification

      Bahri, Maroua; Gomes, Heitor Murilo; Bifet, Albert; Maniu, Silviu
      Thumbnail
      Files
      bahri2020adaptive.pdf
      Accepted version, 362.9Kb
      DOI
       10.1109/IJCNN48605.2020.9207188
      Find in your library  
      Citation
      Export citation
      Bahri, M., Gomes, H. M., Bifet, A., & Maniu, S. (2020). CS-ARF: Compressed adaptive random forests for evolving data stream classification. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). Glasgow, UK: IEEE. https://doi.org/10.1109/IJCNN48605.2020.9207188
      Permanent Research Commons link: https://hdl.handle.net/10289/14221
      Abstract
      Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random Forests (ARF) is a recent ensemble method for evolving data streams that proved to be of a good predictive performance but, as all ensemble methods, it suffers from a severe drawback related to the high computational demand which prevents it from being efficient and further exacerbates with high-dimensional data. In this context, the application of a dimensionality reduction technique is crucial while processing the Internet of Things (IoT) data stream with ultrahigh dimensionality. In this paper, we aim to alleviate this deficiency and improve ARF performance, so we introduce the CS-ARF approach that uses Compressed Sensing (CS) as an internal pre-processing task, to reduce the dimensionality of data before starting the learning process, that will potentially lead to a meaningful improvement in memory usage. Experiments on various datasets show the high classification performance of our CS-ARF approach compared against current state-of-the-art methods while reducing resource usage.
      Date
      2020
      Type
      Conference Contribution
      Publisher
      IEEE
      Rights
      © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
      Collections
      • Computing and Mathematical Sciences Papers [1454]
      Show full item record  

      Usage

      Downloads, last 12 months
      62
       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement