K-Nearest Neighbors Classifier for Field Bit Error Rate Data

被引：0

作者：

Allogba, Stephanie ^{[1
]}

Tremblay, Christine ^{[1
]}

机构：

[1] Ecole Technol Super, Network Technol Lab, Montreal, PQ, Canada

来源：

2018 ASIA COMMUNICATIONS AND PHOTONICS CONFERENCE (ACP) | 2018年

基金：

加拿大自然科学与工程研究理事会;

关键词：

K nearest neighbors; cross-validation; system performance; bit error rate; anomaly detection; cognitive optical network; statistical analysis; NETWORKS;

D O I：

暂无

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Proactive control strategies based on machine learning can be expected to be very useful for detecting anomalies and soft failures in high-capacity optical networks. In this paper, we propose a bit error rate (BER) classifier based on the K-nearest neighbors (KNN) algorithm. The BER classifier has been used for classifying field BER data collected in a 230km optical link of the CANARIE network. The results show that the classification accuracy increases up to 97.8% depending on the features considered in the classification process.

引用

页数：3

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