An iterative design methodology for user-friendly natural language office information applications
ACM Transactions on Information Systems (TOIS)
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction
Data Mining and Knowledge Discovery
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A review of instance selection methods
Artificial Intelligence Review
Multimodal emotion classification in naturalistic user behavior
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Multiple classifier systems for the classificatio of audio-visual emotional states
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments
Studying self- and active-training methods for multi-feature set emotion recognition
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Editorial: Large scale instance selection by means of federal instance selection
Data & Knowledge Engineering
Proceedings of the 15th ACM on International conference on multimodal interaction
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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Affective computing aim to provide simpler and more natural interfaces for human-computer interaction applications, e.g. recognizing automatically the emotional status of the user based on facial expressions or speech is important to model user as complete as possible in order to develop human-computer interfaces that are able to respond to the user's action or behavior in an appropriate manner. In this paper we focus on audio-based emotion recognition. Data sets employed for the statistical evaluation have been collected through Wizard-of-Oz experiments. The emotional labels have been are defined through the experimental set up therefore given on a relatively coarse temporal scale (a few minutes) which This global labeling concept might lead to miss-labeled data at smaller time scales, for instance for window sizes uses in audio analysis (less than a second). Manual labeling at these time scales is very difficult not to say impossible, and therefore our approach is to use the globally defined labels in combination with instance/sample selection methods. In such an instance selection approach the task is to select the most relevant and discriminative data of the training set by using a pre-trained classifier. Mel-Frequency Cepstral Coefficients (MFCC) features are used to extract relevant features, and probabilistic support vector machines (SVM) are applied as base classifiers in our numerical evaluation. Confidence values to the samples of the training set are assigned through the outputs of the probabilistic SVM.