Floating search methods in feature selection
Pattern Recognition Letters
Affective computing
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Scaling Kernel-Based Systems to Large Data Sets
Data Mining and Knowledge Discovery
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Emotion recognition from physiological signals using wireless sensors for presence technologies
Cognition, Technology and Work
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating affective interfaces: innovative approaches
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Empathic painting: interactive stylization through observed emotional state
Proceedings of the 4th international symposium on Non-photorealistic animation and rendering
A fast all nearest neighbor algorithm for applications involving large point-clouds
Computers and Graphics
New Algorithms for Efficient High-Dimensional Nonparametric Classification
The Journal of Machine Learning Research
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Modelling affective-based music compositional intelligence with the aid of ANS analyses
Knowledge-Based Systems
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in Data Mining
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Proceedings of the 2013 international conference on Intelligent user interfaces
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Data-centric affect modeling may render itself restrictive in practical applications for three reasons, namely, it falls short of feature optimization, infers discrete affect classes, and deals with relatively small to average sized datasets. Though it seems practical to use the feature combinations already associated to commonly investigated sensors, there may be other potentially optimal features that can lead to new relations. Secondly, although it seems more realistic to view affect as continuous, it requires using continuous labels that will increase the difficulty of modeling. Lastly, although a large scale dataset reflects a more precise range of values for any given feature, it severely hinders computational efficiency. We address these problems when inferring physiology-affect relations from datasets that contain 2-3 million feature vectors, each with 49 features and labelled with continuous affect values. We employ automatic feature selection to acquire near optimal feature subsets and a fast approximate kNN algorithm to solve the regression problem and cope with the challenge of a large scale dataset. Our results show that high estimation accuracy may be achieved even when the selected feature subset is only about 7% of the original features. May the results here motivate the HCI community to pursue affect modeling without being deterred by large datasets and further the discussions on acquiring optimal features for accurate continuous affect approximation.