Extraction and Early Detection of Anomalies in Lightpath SNR Using Machine Learning Models
Bibliographic record
Abstract
In a context of ever-increasing traffic, a degradation of the optical layer could affect client demands, in particular the quality of service provided by telecommunications operators. Thus, the rapid detection and prediction of performance degradations occurring in the optical lightpath could help to minimize errors in the network. This paper proposes a failure detection model, equivalent to a performance degradation detection model, but based on machine learning (ML) techniques, namely, the interquartile range (IQR) and the support vector machine (SVM) methods. Note that this model is built from performance metrics monitored on real optical lightpaths. In addition, our model can both label the anomalies to be defined on the data and capture the features that will be used. Feature engineering is explored using three ML techniques, namely the Boruta algorithm, the Random Forest classifier and the recursive feature elimination (RFE), to select the most useful features for the implementation of the model. Tested on monitored performance metrics, the validation phase shows that the model using the RFE method gives us the best results with an F1-score and a recall of 99.51% and 100%, respectively. These results prove the model's ability to detect in advance the degradation of the performance of the network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".