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所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：GLOBECOM 2017 - 2017 IEEE GLOBAL COMMUNICATIONS CONFERENCE

关键字：Self-healing Fault diagnosis Imbalanced data Synthetic minority oversampling technique (SMOTE) AdaBoost

摘要：Self-organizing networks (SON) aim to offer high quality services while reducing both capital expenditure (CAPEX) and operational expenditure (OPEX). SON consists of three main functions: self-configuration, self-optimization, and self-healing. Comparing with self-configuration and self-optimization, there exits only few studies on self-healing. However, it plays an important role in maintaining network operation. Note that self-healing mainly includes fault detection, fault diagnosis, and fault compensation. In this paper, we focus on fault diagnosis and propose an ensemble learning based fault diagnosis system for a self-organizing cellular networks. Specifically, in the proposed ensemble learning framework, the base learner is strengthened in each iteration and the final diagnosis result is obtained from the combination of all base classifications. Moreover, traditional classification algorithms are designed considering the premise of balanced data set. However, when they are applied to imbalanced data, the classification accuracy for minority classes is not satisfying. To deal with imbalanced training data sets, we apply the synthetic minority over-sampling technique (SMOTE) in the proposed system, which could also alleviate the difficulties caused by insufficient fault data. Simulation results show that the proposed system can achieve a high diagnosis accuracy, which can be further improved with the increase of training samples. In addition, the diagnosis accuracy of minority fault classes can be significantly improved with the application of SMOTE.

ISSN号：2334-0983

是否译文：否

发表时间：2017-01-01

合写作者：Sun, Mengyun,朱琨,关东海,F70206470

通讯作者：钱红燕