Context-Aware Feature Selection using Denoising Auto-Encoder for Fault Detection in Cloud Environments
Bibliographic record
Abstract
Machine learning is expected to play an instrumental role in automating the detection of faults in next-generation cloud networks. The existing machine-learning-based fault detection methods suffer from the following drawbacks: (i) ignoring the issue of missing feature values, (ii) ignoring the impact of each feature on output prediction over other features (measurement of feature importance), and (iii) lack of calculation of the proper number of features for fault detection. To address the above challenges, in this paper, we propose a context-aware feature selection method to improve the performance of fault detection methods in the cloud environment, aiming at maximizing the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -score. Our proposed solution comprises Denoising Auto-Encoder (DAE) stacked with a Discriminative Model (DM). The DAE is applied to handle the missing feature values and encoding features while the DM is responsible for making predictions of system status based on the encoded features. Then, the sensitivity analysis of output prediction with respect to each input feature value is used to measure the feature importance. We compare our work with existing solutions from the literature. Our results reveal that the proposed solution can improve the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -score up to 47 % and 76 % in the scenario where all feature values are known and in the scenario where only 25 % of feature values are known, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".