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Genetic search feature selection for affective modeling: a case study on reported preferences

Published: 29 October 2010 Publication History

Abstract

Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built. The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method is capable of picking subsets of features that generate more accurate affective models.

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cover image ACM Conferences
AFFINE '10: Proceedings of the 3rd international workshop on Affective interaction in natural environments
October 2010
106 pages
ISBN:9781450301701
DOI:10.1145/1877826
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 October 2010

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Author Tags

  1. affective modeling
  2. feature selection
  3. genetic search
  4. preference learning

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MM '10
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MM '10: ACM Multimedia Conference
October 29, 2010
Firenze, Italy

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  • (2023)Affective Game Computing: A SurveyProceedings of the IEEE10.1109/JPROC.2023.3315689111:10(1423-1444)Online publication date: Oct-2023
  • (2022)RankNEATProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528744(1084-1092)Online publication date: 8-Jul-2022
  • (2016)Survey on Feature Extraction and Applications of BiosignalsMachine Learning for Health Informatics10.1007/978-3-319-50478-0_8(161-182)Online publication date: 10-Dec-2016
  • (2016)Psychophysiology in GamesEmotion in Games10.1007/978-3-319-41316-7_7(119-137)Online publication date: 4-Nov-2016
  • (2015)Using High-Frequency Interaction Events to Automatically Classify Cognitive LoadHuman Behavior, Psychology, and Social Interaction in the Digital Era10.4018/978-1-4666-8450-8.ch010(210-228)Online publication date: 2015
  • (2015)A New Approach for Wrapper Feature Selection Using Genetic Algorithm for Big DataIntelligent and Evolutionary Systems10.1007/978-3-319-27000-5_6(75-83)Online publication date: 12-Nov-2015
  • (2014)Deep Multimodal FusionProceedings of the 16th International Conference on Multimodal Interaction10.1145/2663204.2663236(34-41)Online publication date: 12-Nov-2014
  • (2014)Don’t Classify Ratings of Affect; Rank Them!IEEE Transactions on Affective Computing10.1109/TAFFC.2014.23522685:3(314-326)Online publication date: 1-Jul-2014
  • (2013)Fusing Visual and Behavioral Cues for Modeling User Experience in GamesIEEE Transactions on Cybernetics10.1109/TCYB.2013.227173843:6(1519-1531)Online publication date: Dec-2013
  • (2013)Learning deep physiological models of affectIEEE Computational Intelligence Magazine10.1109/MCI.2013.22478238:2(20-33)Online publication date: 1-May-2013
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