Research Article
Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine
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@INPROCEEDINGS{10.1007/978-3-030-51103-6_24, author={Liying Wang and Zhiqiang Xu}, title={Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Hearing loss identification Fractional fourier transform entropy Direct acyclic graph support vector machine}, doi={10.1007/978-3-030-51103-6_24} }- Liying Wang
Zhiqiang Xu
Year: 2020
Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_24
Abstract
With the risk of hearing loss being higher than before since the digital device is more popular, it becomes more urgent to identify the sensorineural hearing loss from the view of changes in internal brain structure. Based on 180 brain MRI of three categories of hearing loss balanced dataset, one schema with fractional Fourier transform entropy and direct acyclic graph support vector machine is proposed and applied to identify the features and predict the categories of hearing loss. The experiments prove this schema rather promising when the dataset is not large since the overall accuracy is up to 94.06 ± 1.08% which is higher than those of some previous methods in scope of traditional machine learning.
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