A Cluster-Based Prototype Reduction for Online Classification

  • Kemilly Dearo Garcia
  • André C. P. L. F. de Carvalho
  • João Mendes-MoreiraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)


Data stream is a challenging research topic in which data can continuously arrive with a probability distribution that may change over time. Depending on the changes in the data distribution, different phenomena can occur, for example, a concept drift. A concept drift occurs when the concepts associated with a dataset change when new data arrive. This paper proposes a new method based on k-Nearest Neighbors that implements a sliding window requiring less instances stored for training than existing methods. For such, a clustering approach is used to summarize data by placing labeled instances considered similar in the same cluster. Besides, instances close to the uncertainty border of existing classes are also stored, in a sliding window, to adapt the model to concept drift. The proposed method is experimentally compared with state-of-the-art classifiers from the data stream literature, regarding accuracy and processing time. According to the experimental results, the proposed method has better accuracy and less time consumption when fewer information about the concepts are stored in a single sliding window.


kNN Prototyping Data stream Online clustering 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of TwenteEnschedeNetherlands
  2. 2.ICMC, University of São PauloSão PauloBrazil
  3. 3.Faculty of EngineeringUniversity of PortoPortoPortugal
  4. 4.LIAAD-INESC TECPortoPortugal

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