Abstract
In big data analytics and machine learning applications on telecom network measurement data, accuracy of findings during the analysis phase greatly depends on the quality of the training data set. If the training data set contains data from Network Elements (NEs) with high number of failures and high failure rates, such behavior will be assumed as normal. As a result, the analysis phase will fail to detect NEs with such behavior. High failure ratios have traditionally been considered as signs of faults in NEs. Operators use well-known Key Performance Indicators (KPIs), such as, e.g., Drop Call Ratio and Handover failure ratio to identify misbehaving NEs. The main problem with these KPIs based on failure ratios is their unstable nature. This paper proposes a method of measuring the significance of failures and its use in training set filtering.
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Jerome, R.B., Hätönen, K. (2015). A Metric for Determining the Significance of Failures and Its Use in Anomaly Detection. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_17
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DOI: https://doi.org/10.1007/978-3-319-23983-5_17
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