AI Article Synopsis
- * Different methods, including univariate selection and recursive feature elimination, were tested alongside machine learning algorithms like linear regression, random forest, and support vector machine.
- * The univariate selection method combined with support vector machine yielded the best accuracy at 75%, indicating promise for future research on the octave illusion and automated classification algorithms.
Article Abstract
The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512176 | PMC |
| http://dx.doi.org/10.3390/s21196407 | DOI Listing |
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Article Synopsis
- * Different methods, including univariate selection and recursive feature elimination, were tested alongside machine learning algorithms like linear regression, random forest, and support vector machine.
- * The univariate selection method combined with support vector machine yielded the best accuracy at 75%, indicating promise for future research on the octave illusion and automated classification algorithms.
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