Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Novel Methods for Subset Selection with Respect to Problem Knowledge
IEEE Intelligent Systems
Introducing a Family of Linear Measures for Feature Selection in Text Categorization
IEEE Transactions on Knowledge and Data Engineering
The implementation of the emotion recognition from speech and facial expression system
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Modeling timbre distance with temporal statistics from polyphonic music
IEEE Transactions on Audio, Speech, and Language Processing
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This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merit regarding pattern recognition performance. Thus, we developed a method called an ‘Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. Our innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an ‘Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection(SFS) and Genetic Algorithm Feature Selection(GAFS), we verified that the top three features were better than the randomly selected feature set.