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• W2734606312 abstract "System availability is one of the major requirements expected from systems in the trading domain. In order to prevent system outages that can deteriorate system availability, anomaly detection must be able to assess the status of the system and detect anomalies that can lead to failures on a real-time basis. This paper presents a framework for anomaly detection for complex trading systems based on supervised learning approaches. Multiple feature reduction techniques were experimented with, in order to eliminate the noisy features that were initially derived from the system parameters. A classification technique based on Radial Basis Function (RBF) kernel Support Vector Machine (SVM) along with a feature selection technique built on a tree-based ensemble displayed the most promising results." @default.
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• W2734606312 date "2017-05-01" @default.
• W2734606312 modified "2023-09-27" @default.
• W2734606312 title "Anomaly detection in complex trading systems" @default.
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• W2734606312 doi "https://doi.org/10.1109/mercon.2017.7980524" @default.
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