Weighted IForest and siamese GRU on small sample anomaly detection in healthcare. (May 2022) - Wang, Junfeng; Jia, Yan; Wang, Dongbo; Xiao, Wenjing; Wang, Zhenfei
Highlights: Proposes a weighted IForest algorithm to mark a small part of the data. Expert decision making rules and use logical regression algorithm to obtain the weight of features. Improves the FDA function and uses it as the loss function of SGRU to improve the accuracy of the algorithm. Abstract: Background and objective At present, many achievements have been made in anomaly detection of big data using deep neural network, However, in many practical application scenarios, there are still some problems, such as shortage of data, too large workload of manual data annotating and so on. Methods This paper proposes weighted iForest and Siamese GRU (WIF-SGRU) algorithm on small sample anomaly detection. In the data annotation stage, we propose a weighted IForest algorithm for automatic annotation of unlabeled data. In the training phase of anomaly detection model, the Siamese GRU is proposed to train the target data to obtain the anomaly model and detect the real-time anomaly of small sample data. Results The proposed algorithm is verified on six public datasets (Arrhythmia, Shuttle, Staellite, Sttimage-2, Lymphography, and WBC). The experimental results show that compared with the traditional data annotation and anomaly detection algorithm, the algorithm of weighted IForest and Siamese GRU improves the accuracy and real-time performance. Conclusions This paper proposes a weighted IForest and Siamese GRU algorithm architecture, which provides a more accurate and efficient method for outlier detection of data. Firstly, the framework uses the improved IForest algorithm to label the label-free data, Then the Siamese GRU is optimized by the improved F D A loss function, the optimized network is used to learn the distance between data for real-time and efficient anomaly detection. Experiments show that the framework has good potential.