首页|Khon Kaen University Researchers Focus on Support Vector Machines (Comparison of support vector machine and random forest algorithms for classification of songs for relaxation purposes in individuals with stress disorders)
Khon Kaen University Researchers Focus on Support Vector Machines (Comparison of support vector machine and random forest algorithms for classification of songs for relaxation purposes in individuals with stress disorders)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on . According t o news reporting originating from Khon Kaen University by NewsRx correspondents, research stated, "The research compares the performance of support vector machi ne (SVM) and random forest algorithms in identifying songs suitable for relaxati on in patients with stress problems." Our news editors obtained a quote from the research from Khon Kaen University: " The dataset comprises both Thai and international songs categorized into therapy and non-therapy groups. The results demonstrate that the support vector machine achieves an accuracy of 78%, outperforming the random forest with an accuracy of 72%. Precision and F1-score metrics further emphasiz e the superiority of the support vector machine in classification. Notably, the support vector machine has recall rates of 50% and 100% for therapy and non-therapy classes, respectively, while the random forest has r ecall from class therapy of 38% and class non-therapy of 100% . The findings suggest that providing individuals with stress issues the opportu nity to listen to stress-reducing music can be a viable approach to reducing the need for psychiatric therapy."
Khon Kaen UniversityAlgorithmsEmergi ng TechnologiesMachine LearningSupport Vector MachinesTherapyVector Mach ines
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