Application of Dimensionality Reduction Methods for Eye Movement Data Classification

  • Aleksandra Gruca
  • Katarzyna Harezlak
  • Pawel Kasprowski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 391)


In this paper we apply two data dimensionality reduction methods to eye movement dataset and analyse how the feature reduction method improves classification accuracy. Due to the specificity of the recording process, eye movement datasets are characterized by both big size and high-dimensionality that make them difficult to analyse and classify using standard classification approaches. Here, we analyse eye movement data from BioEye 2015 competition and to deal with the problem of high dimensionality we apply SVM combined with PCA feature extraction and random forests wrapper variable selection. Our results show that the reduction of the number of variables improves classification results. We also show that some of classes (participants) can be classified (recognised) with high accuracy while others are very difficult to be correctly identified.


Eye movement data analysis DTW Dimensionality reduction  Classification PCA SVM Random forest 



The work was partially supported by National Science Centre (decision DEC-2011/01/D/ST6/07007) (A.G). Computations were performed with the use of the infrastructure provided by the NCBIR POIG.02.03.01-24-099/13 grant: GCONiI - Upper-Silesian Center for Scientific Computations.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Aleksandra Gruca
    • 1
  • Katarzyna Harezlak
    • 1
  • Pawel Kasprowski
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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