Feature Selection Using Maximum Feature Tree Embedded with Mutual Information and Coefficient of Variation for Bird Sound Classification

Haifeng Xu, Yan Zhang, Jiang Liu and Danjv Lv

Mathematical Problems in Engineering, 2021, vol. 2021, 1-14

Abstract:

The classification of bird sounds is important in ecological monitoring. Although extracting features from multiple perspectives helps to fully describe the target information, it is urgent to deal with the enormous dimension of features and the curse of dimensionality. Thus, feature selection is necessary. This paper proposes a scoring feature method named MICV (Mutual Information and Coefficient of Variation), which uses the coefficient of variation and mutual information to evaluate each feature’s contribution to classification. And then, a method named ERMFT (Eliminating Redundancy Based on Maximum Feature Tree) based on two neighborhoods to eliminate redundancy to optimize features is explored. These two methods are combined as the MICV-ERMFT method to select the optimal features. Experiments are conducted to compare eight different feature selection methods with two sounds datasets of bird and crane. Results show that the MICV-ERMFT method outperforms other feature selection methods in the accuracy of the classification and is less time-consuming.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2021/8872248.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2021/8872248.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8872248

DOI: 10.1155/2021/8872248

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().