Ensemble Classification Based on Feature Selection for Environmental Sound Recognition

Environmental sound recognition has been a hot topic in the domain of audio recognition. How to select the optimal feature subsets and enhance the performance of classification precisely is an urgent problem to be solved. Ensemble learning, a new kind of method presented recently, has been an effect...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019-01, Vol.2019 (2019), p.1-7
Main Authors: Han, Te, Xu, Haifeng, Zhang, Yan, Zhao, Shuai
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Background noise
Bioinformatics
Classification
Data analysis
Data mining
Datasets
Feature recognition
Gene expression
International conferences
Methods
Pattern recognition
Performance evaluation
Product acceptance
Sound
Surveillance
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Summary:Environmental sound recognition has been a hot topic in the domain of audio recognition. How to select the optimal feature subsets and enhance the performance of classification precisely is an urgent problem to be solved. Ensemble learning, a new kind of method presented recently, has been an effective way to improve the accuracy of classification in feature selection. In this paper, experiments were performed on environmental sound dataset. An improved method based on constraint score and multimodels ensemble feature selection methods (MmEnFs) were exploited in the experiments. The experimental results show that when enough attributes are selected, the improved method can get a better performance compared to other feature selection methods. And the ensemble feature selection method, which combines other methods, can obtain the optimal performance in most cases.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/4318463