Ensemble Classification Based on Feature Selection for Environmental Sound Recognition
Joint Authors
Zhao, Shuai
Zhang, Yan
Xu, Haifeng
Han, Te
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-27
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhao, Shuai& Zhang, Yan& Xu, Haifeng& Han, Te. 2019. Ensemble Classification Based on Feature Selection for Environmental Sound Recognition. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1195590
Modern Language Association (MLA)
Zhao, Shuai…[et al.]. Ensemble Classification Based on Feature Selection for Environmental Sound Recognition. Mathematical Problems in Engineering No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1195590
American Medical Association (AMA)
Zhao, Shuai& Zhang, Yan& Xu, Haifeng& Han, Te. Ensemble Classification Based on Feature Selection for Environmental Sound Recognition. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1195590
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195590