Abstract
One-class support vector machine (OCSVM) is one of the most popular algorithms in the one-class classification problem, but it has one obvious disadvantage: it is sensitive to noise. In order to solve this problem, the fuzzy membership degree is introduced into OCSVM, which makes the samples with different importance have different influences on the determination of classification hyperplane and enhances the robustness. In this paper, a new calculation method of membership degree is proposed and introduced into the fuzzy multiple kernel OCSVM (FMKOCSVM). The combined kernel is used to measure the local similarity between samples, and then, the importance of samples is determined based on the local similarity between training samples, so as to determine the membership degree and reduce the impact of noise. The proposed membership requires only positive data in the calculation process, which is consistent with the training set of OCSVM. In this method, the noise has a smaller membership value, which can reduce the negative impact of noise on the classification boundary. Simultaneously, this method of calculating membership has a higher efficiency. The experimental results show that FMKOCSVM based on proposed local similarity membership is efficient and more robust to outliers than the ordinary multiple kernel OCSVMs.
Highlights
Detection is an important aspect of data mining
WMKOCSVM: the weighted one-class support vector machine is formed by WOCSVM [14] combined with the multiple kernel function; (3) FMKOCSVM: the fuzzy multiple kernel one-class support vector machine, in which membership is calculated based on a rough set [19]
In order to solve the problem of poor robustness of MKOCSVM, this paper proposes a fuzzy multiple kernel one-class support vector machine based on local similarity, in which membership is based on the local similarity of the training data
Summary
Detection is an important aspect of data mining. The application of anomaly detection in the field of medicine and biological systems is of great significance, and it has been successfully applied to protein detection, [1] cancer screening, [2] and health monitoring [3]. E essence of anomaly detection is a classification algorithm suitable for processing data with an extremely imbalanced class. Complex biological systems usually have this feature. The data of an infectious disease model may include characteristic data of patients and characteristic data of nonpatients. In real life, there are far more healthy people than patients. And effective detection of patients with infectious diseases is an effective way to prevent the outbreak of infectious diseases
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