Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
International Journal of Human-Computer Studies
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
Proceedings of the 5th ACM SIGGRAPH Symposium on Video Games
Towards affective camera control in games
User Modeling and User-Adapted Interaction
Genetic search feature selection for affective modeling: a case study on reported preferences
Proceedings of the 3rd international workshop on Affective interaction in natural environments
Protein sequence classification through relevant sequence mining and bayes classifiers
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
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Temporal data from multimodal interaction such as speech and bio-signals cannot be easily analysed without a preprocessing phase through which some key characteristics of the signals are extracted. Typically, standard statistical signal features such as average values are calculated prior to the analysis and, subsequently, are presented either to a multimodal fusion mechanism or a computational model of the interaction. This paper proposes a feature extraction methodology which is based on frequent sequence mining within and across multiple modalities of user input. The proposed method is applied for the fusion of physiological signals and gameplay information in a game survey dataset. The obtained sequences are analysed and used as predictors of user affect resulting in computational models of equal or higher accuracy compared to the models built on standard statistical features.