An introduction to variable and feature selection
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Overfitting in making comparisons between variable selection methods
The Journal of Machine Learning Research
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
LS Bound based gene selection for DNA microarray data
Bioinformatics
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
Non-parametric classifier-independent feature selection
Pattern Recognition
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Analyze multiple emotional expressions in a sentence
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Segment-based emotion recognition from continuous Mandarin Chinese speech
Computers in Human Behavior
Questionnaire- versus voice-based screening for laryngeal disorders
Expert Systems with Applications: An International Journal
International Journal of Speech Technology
Class-specific multiple classifiers scheme to recognize emotions from speech signals
Computer Speech and Language
| Hi-index | 12.05 |
This paper deals with the strategies for feature selection and multi-class classification in the emotion detection problem. The aim is two-fold: to increase the effectiveness of four feature selection algorithms and to improve accuracy of multi-class classifiers for emotion detection problem under different frameworks and strategies. Although, a large amount of research has been conducted to determine the most informative features in emotion detection, it is still an open problem to identify reliably discriminating features. As it is believed that highly informative features are more critical factor than classifier itself, recent studies have been focused on identifying the features that contribute more to the classification problem. In this paper, in order to improve the performance of multi-class SVMs in emotion detection, 58 features extracted from recorded speech samples are processed in two new frameworks to boost the feature selection algorithms. Evaluation of the final feature sets validates that the frameworks are able to select more informative subset of the features in terms of class-separability. Also it is found that among four feature selection algorithms, a recently proposed one, LSBOUND, significantly outperforms the others. The accuracy rate obtained in the proposed framework is the highest achievement reported so far in the literature for the same dataset.