@Article{cmes.2020.011069,
AUTHOR = {Chaosheng Tang, Deepak Ranjan Nayak, Shuihua Wang},
TITLE = {Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {1},
PAGES = {299--313},
URL = {http://www.techscience.com/CMES/v125n1/40217},
ISSN = {1526-1506},
ABSTRACT = {Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing,
misunderstanding, and communication problems. Therefore, it is necessary
to provide early diagnosis and timely treatment for HL. This study investigated
the advantages and disadvantages of three classical machine learning
methods: multilayer perceptron (MLP), support vector machine (SVM), and
least-square support vector machine (LS-SVM) approach and made a further
optimization of the LS-SVM model via wavelet entropy. The investigation
illustrated that themultilayer perceptron is a shallowneural network,while the
least square support vector machine uses hinge loss function and least-square
optimization method. Besides, a wavelet selection method was proposed, and
we found db4 can achieve the best results. The experiments showed that
the LS-SVM method can identify the hearing loss disease with an overall
accuracy of three classes as 84.89 ± 1.77, which is superior to SVM and MLP.
The results show that the least-square support vector machine is effective in
hearing loss identification.},
DOI = {10.32604/cmes.2020.011069}
}