https://scholars.lib.ntu.edu.tw/handle/123456789/581423
標題: | System Error Prediction for Business Support Systems in Telecommunications Networks | 作者: | Yeh E.-H Lin P Lin X.-X Jeng J.-Y Fang Y. PHONE LIN |
關鍵字: | Decision trees; Deep learning; Errors; Forecasting; Learning algorithms; Business support systems; Feature creation; Important features; Prediction performance; Random forest algorithm; Reliability and stability; Telecommunications networks; Time series informations; Learning systems | 公開日期: | 2020 | 卷: | 31 | 期: | 11 | 起(迄)頁: | 2723-2733 | 來源出版物: | IEEE Transactions on Parallel and Distributed Systems | 摘要: | Reliability and stability have been treated as the major requirements for the Business Support System (BSS) in telecommunications networks. It is crucial and essential for service providers to maintain good operating state of the BSS. In this article, we aim at system error prediction for a BSS, i.e., we predict occurrences of the abnormal state or behavior of the BSS. Because the occurrences of system errors are rare events in the BSS (i.e., the dataset of system status is highly imbalanced), it is highly challenging to use machine learning or deep learning algorithms to predict system error for the BSS. To address this challenge, we propose a machine learning-based framework for the system error prediction and a Frequency-based Feature Creation (FFC) algorithm to create new features to improve prediction. By adding the time-series information created by the existing features, the proposed FFC can amplify the effects of important features. Our experimental results show that the FFC significantly improves the prediction performance for the Random Forest algorithm. ? 1990-2012 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087484086&doi=10.1109%2fTPDS.2020.3001593&partnerID=40&md5=52a1f1bdb5e5e219beacf8ba1824af7f https://scholars.lib.ntu.edu.tw/handle/123456789/581423 |
ISSN: | 10459219 | DOI: | 10.1109/TPDS.2020.3001593 |
顯示於: | 資訊工程學系 |
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