A SVM-based classification approach for early warning systems

Publication Type:
Conference Proceeding
Citation:
World Scientific Proceedings Series on Computer Engineering and Information Science 1; Computational Intelligence in Decision and Control - Proceedings of the 8th International FLINS Conference, 2008, pp. 549 - 554
Issue Date:
2008-12-01
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An early warning system (EWS) is a timely surveillance tool to identifies potential crises and generate warning signals at a relatively early stage. This study aims to improve the learning functions of an EWS through training it using support vector machine (SVM) techniques. An adaptive pruning algorithm of SVM classification is developed which can improve prediction ability of EWS. This algorithm also can handle multi-data sources, multi-sensitive values, multi-indicators, and multi-crises issues in EWSs.
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