Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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Proceedings of the 6th international conference on Multimodal interfaces
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IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
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Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Fundamentals of physiological computing
Interacting with Computers
Affective game engines: motivation and requirements
Proceedings of the 4th International Conference on Foundations of Digital Games
Generic physiological features as predictors of player experience
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Learning general preference models from physiological responses in video games: how complex is it?
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Mining multimodal sequential patterns: a case study on affect detection
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
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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.