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Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion

  • Christina Schweikert
  • Louis Gobin
  • Shuxiao Xie
  • Shinsuke Shimojo
  • D. Frank Hsu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

Face image preference is influenced by many factors and can be detected by analyzing eye movement data. When comparing two face images, our gaze shifts within and between the faces. Eye tracking data can give us insights into the cognitive processes involved in forming a preference. In this paper, a gaze tracking dataset is analyzed using three machine learning algorithms (MLA): AdaBoost, Random Forest, and Mixed Group Ranks (MGR) as well as a newly developed machine learning framework called Multi-Layer Combinatorial Fusion (MCF) to predict a subject’s face image preference. Attributes constructed from the dataset are treated as input scoring systems. MCF involves a series of layers that consist of expansion and reduction processes. The expansion process involves performing exhaustive score and rank combinations, while the reduction process uses performance and diversity to select a subset of systems that will be passed onto the next layer of analysis. Performance and cognitive diversity are used in weighted scoring system combinations and system selection. The results outperform the Mixed Group Ranks algorithm, as well as our previous work using pairwise scoring system combinations.

Keywords

Combinatorial Fusion Analysis (CFA) Multi-layer Combinatorial Fusion (MCF) Cognitive diversity Machine learning Preference detection 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christina Schweikert
    • 1
  • Louis Gobin
    • 2
  • Shuxiao Xie
    • 2
  • Shinsuke Shimojo
    • 3
  • D. Frank Hsu
    • 2
  1. 1.Division of Computer Science, Mathematics and ScienceSt. John’s UniversityQueensUSA
  2. 2.Laboratory of Informatics and Data Mining, Department of Computer and Information ScienceFordham UniversityNew YorkUSA
  3. 3.Division of Biology and Biological Engineering/Computation and Neural SystemsCalifornia Institute of TechnologyPasadenaUSA

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