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      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      GOOWE-ML: a novel online stacked ensemble for multi-label classification in data streams

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      Author(s)
      Büyükçakır, Alican
      Advisor
      Can, Fazlı
      Date
      2019-07
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemblebased methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multilabel classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.
      Keywords
      Multi-label
      Data stream
      Supervised learning
      Classification
      Ensemble learning
      Stacking
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      http://hdl.handle.net/11693/52288
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      • Dept. of Computer Engineering - Master's degree 540
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