Abstract:We present an automated system for the diagnosis of both known and unknown soft-failures in end-user devices (UDs). Known faults that cause network performance degradation are used to train the classifier-based system in a supervised manner while unknown faults are automatically detected and clustered to identify the existence of new categories of soft-failures. The supervised classifier used in the system can be retrained by including the newly detected faults to enhance its performance. The system uses 460 f… Show more
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