Context-aware Feature Selection using RNN DAE-RNN DM for Fault Detection in Cloud
Bibliographic record
Abstract
Machine Learning (ML) is expected to have an important role in automating the detection of faults in next-generation cloud networks. In ML-based fault detection methods, an efficient method is needed to select the most important features in the presence of missing and/or unknown feature values. In this paper, we propose a context-aware feature selection method, which uses a sensitivity analysis to measure the impact of features on prediction accuracy. Given a time-series, multivariate, and incomplete data, our feature selection method uses Recurrent Neural Networks (RNN) and Auto-Encoder (AE) with an RNN and discriminative model (DM) to estimate the missing features and their sensitivities. The RNN and DAE handle time-series data and missing feature values, respectively, while the RNN DM predicts the system status. Our evaluations on the real-world Google cluster dataset shows that the proposed method outperforms the existing methods in terms of F1-score.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".