Abstract
Aging of sensors, faults in the read-out electronics and environmental changes are some immediate examples of time variant mechanisms violating that stationarity hypothesis mostly assumed in the design of classification systems. Such changes, known in the related literature as concept drift, modify the probability density function of measurements, hence impairing the accuracy of the classifier. To cope with these mechanisms, active classifiers such as the Just-in-time adaptive ones, are needed to detect a change in stationarity and modify the classifier configuration accordingly to track the process evolution. At the same time, when the process is stationary, new available supervised information is integrated in the classifier to improve over time its classification accuracy. This paper introduces a JIT adaptive classifier based on support vector machines able to track changes in the process generating the data with computational complexity and memory requirements well below that of current JIT classifiers integrating k-nearest neighbor solutions.
This work was supported partly by National Natural Science Foundation of China (Nos. 61273136, 61034002, 60921061), Beijing Natural Science Foundation (4122083) and visiting professorship of Chinese Academy of Sciences.
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Alippi, C., Bu, L., Zhao, D. (2012). SVM-Based Just-in-Time Adaptive Classifiers. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_81
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DOI: https://doi.org/10.1007/978-3-642-34481-7_81
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