Efficient Data Point Pruning for One-Class SVM

Fujiwara, Yasuhiro; Kanai, Sekitoshi; Arai, Junya; Ida, Yasutoshi

doi:10.1609/aaai.v33i01.33013590

Cited by 3 publications

(1 citation statement)

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“…Therefore, these models do not require labeled datasets and can avoid category imbalance issues. One-class SVM[30] is a classical machine learning algorithm for detecting anomaly. It builds the boundary Zhaoyi Zhong et albetween normal samples and abnormal samples through hyperplane.…”

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confidence: 99%

Time Series Classification for Portable Medical Devices

Zhong

Sun

Subramani

et al. 2023

EAI Endorsed Scal Inf Syst

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INTRODUCTION: With the continuous progress of the medical Internet of Things, intelligent medical wearable devices are also gradually mature. Among them, medical wearable devices for arrhythmia detection have broad application prospects. Arrhythmia is a common cardiovascular disease. Arrhythmia causes millions of deaths every year and is one of the most noteworthy diseases. Medical mobile information systems (MMIS) provide many ECG signals, which can be used to train deep models to detect arrhythmia automatically. OBJECTIVES: Using deep models to detect arrhythmia is a research hot spot. However, the current algorithms for arrhythmia detection lack of attention to the unsupervised depth model. And they usually build a large comprehensive model for all users for arrhythmia detection, which has low flexibility and cannot extract personalized features from users. Therefore, this paper proposes a personalized arrhythmia detection system based on attention mechanism called personAD. METHODS: The personAD contains four modules: (1) Preprocessing module; (2) Training module; (3) Arrhythmia detection module and (4) User registration module. The personAD trains a separate autoencoder for each user to detect personalized arrhythmia. Using autoencoder to detect arrhythmia can avoid the imbalance of training data. The autoencoder combines a convolutional network and two attention mechanisms. RESULTS: Based on the results on MIT-BIH Arrhythmia Database, we can find that our arrhythmia detection system achieve 98.03% and 99.32% respectively. CONCLUSION: The personAD can effectively detect arrhythmia in ECG signals. The personAD has higher flexibility, and can easily modify the autoencoders for detecting arrhythmia for users.

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confidence: 99%