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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/60667
Title: Application of sorted metrics based on insole feature mining
Authors: Li, H.
Tsviatkou, V. Yu.
Keywords: материалы конференций;correlation coefficient;Bayes factor;informativeness of features
Issue Date: 2025
Publisher: БГУИР
Citation: Li, H. Application of sorted metrics based on insole feature mining / H. Li, V. Yu. Tsviatkou // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, апрель 2025 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: В. Ю. Цветков [и др.]. – Минск, 2025. – С. 155–157.
Abstract: To improve the accuracy and real time performance of fall risk assessment, this study proposes a feature selection method based on sorted metrics. The method evaluates previously generated spatiotemporal features using correlation coefficient (CCF), bayes factor (BF), and selfinformation index (SII) to identify the most informative features. The results show that these metrics play complementary roles in the feature selection process. CCF measures the linear correlation between features and helps eliminate redundancy. BF emphasizes the statistical significance between high-risk and low-risk groups. SII captures distributional differences from the perspective of information entropy. Comparative analysis across various metrics demonstrates that the combined method achieves high accuracy across multiple performance indicators while significantly reducing computational complexity.
URI: https://libeldoc.bsuir.by/handle/123456789/60667
Appears in Collections:Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2025)

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