A Fault Forecasting Approach Using Two-Dimensional Optimization
Bibliographic record
Abstract
Data preparation is crucial in every machine learning approach, particularly when applied to detecting claims in the automotive industry. The challenge of managing highdimensional feature spaces and imbalanced data becomes even more pronounced with the proliferation of IoT devices, which generate vast amounts of data over time. Machine learning models trained on such imbalanced datasets often yield unreliable and inaccurate predictions. Thus, addressing these issues during the data pre-processing phase is critical. In this paper, we introduce a novel two-dimensional optimization (TDO) strategy to tackle the problem of imbalanced data in fault detection, specifically in the context of IoT-enhanced automotive systems. We leverage a heuristic optimization technique known as the Genetic Algorithm to simultaneously reduce both the number of data point tuples and the feature space. Additionally, we evaluate the effectiveness of two-dimensional reduction using Particle Swarm Optimization (PSO) and Whale Optimization algorithms. Our empirical results, derived from data collected from thousands of IoT-equipped vehicles, demonstrate the promise of our proposed methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".