An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm
Bibliographic record
Abstract
In recent years, the prediction of automobile noise has attracted much attention from the academia. This paper aims to develop an automobile noise prediction model based on basic vehicle parameters and vehicle speed, rather than the features calculated from noise data. First, the extension data mining (EDM) was adopted to preprocess the original data, select the features and establish the matter-element models of automobile noise. Then, the extremum entropy method (EEM), an improved information entropy method, was selected to determine the weight of each feature for the calculation of comprehensive correlation function. To verify its effectiveness of the proposed model, our model was applied to experiments on an automobile noise dataset, in comparison with logistic regression and decision tree. The results show that our model outperformed the contrastive methods in prediction accuracy, eliminating the need to set hyper-parameters in advance. The research findings provide a new method to predict automobile noise based on basic vehicle parameters and vehicle speed.
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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 | 0.000 | 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 | 0.001 |
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 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".