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

Schweikert, Christina and Gobin, Louis and Xie, Shuxiao and Shimojo, Shinsuke and Hsu, D. Frank (2018) Preference Prediction Based on Eye Movement Using Multi-layer Combinatorial Fusion. In: Brain Informatics International Conference, BI 2018 Arlington, TX, USA, December 7–9, 2018 Proceedings. Lecture Notes in Computer Science. No.11309. Springer , Cham, Switzerland, pp. 282-293. ISBN 9783030055868. http://resolver.caltech.edu/CaltechAUTHORS:20190328-115920710

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-030-05587-5_27DOIArticle
Additional Information:© Springer Nature Switzerland AG 2018.
Subject Keywords:Combinatorial Fusion Analysis (CFA); Multi-layer Combinatorial Fusion (MCF); Cognitive diversity; Machine learning; Preference detection
Record Number:CaltechAUTHORS:20190328-115920710
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190328-115920710
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94249
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Mar 2019 22:00
Last Modified:05 Apr 2019 22:04

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