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A First Attempt on Online Data Stream Classifier Using Context

  • Michał WoźniakEmail author
  • Bogusław Cyganek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9714)

Abstract

The big data is characterized by 4Vs (volume, velocity, variety, and variability). In this paper we focus on the velocity, but actually it usually comes together with volume. It means, that the crucial problem of the contemporary data analytics is to answer the question how to discover useful knowledge from fast incoming data. The paper presents an online data stream classification method, which adapts the classification with context to recognize incoming examples and additionally takes into consideration the memory and processing time limitations. The proposed method was evaluated on the real medical diagnosis task. The preliminary results of the experiments encourage us to continue works on the proposed approach.

Keywords

Data stream Classification with context Pattern classification 

Notes

Acknowlegements

This work was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/). This work was also supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264. All computer experiments were carried out using computer equipment sponsored by ENGINE project.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1. Faculty of Electronics, Department of Systems and Computer NetworksWrocław University of Science and TechnologyWrocławPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland
  3. 3.ENGINE CenterWrocław University of Science and TechnologyWrocławPoland

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