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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      BELS: a broad ensemble learning system for data stream classification

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      Author(s)
      Bakhshi, Sepehr
      Advisor
      Can, Fazlı
      Date
      2021-12
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      Data stream classification has become a major research topic due to the increase in temporal data. One of the biggest hurdles of data stream classification is the development of algorithms that deal with evolving data, also known as concept drifts. As data changes over time, static prediction models lose their validity. Adapting to concept drifts provides more robust and better performing models. The Broad Learning System (BLS) is an effective broad neural architecture recently developed for incremental learning. BLS cannot provide instant response since it requires huge data chunks and is unable to handle concept drifts. We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves bestin- class model accuracy. It employs a dynamic output ensemble layer to address the limitations of BLS. We present its mathematical derivation, provide comprehensive experiments with 11 datasets that demonstrate the adaptability of our model, including a comparison of our model with BLS, and provide parameter and robustness analysis on several drifting streams, showing that it statistically significantly outperforms seven state-of-the-art baselines. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.
      Keywords
      Data stream mining
      Concept drift
      Ensemble learning
      Neural networks
      Big data
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      http://hdl.handle.net/11693/76862
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      • Dept. of Computer Engineering - Master's degree 566
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