An Inference Method for Personalized Automotive Service Based on Rough Set and Evidential Reasoning
Bibliographic record
Abstract
With the increasing development of China's automobile market, the automotive service profits have become a major part of the industry's profits.However, the after-sale service is still on passive service mode.This mode has some limitations, such as the low service quality of the recommended service items and the lack of personalized service, which seriously affect the quality of the automotive service.In order to solve problems, such as lack of personalized service in the current automotive service mode, an inference method for personalized automotive service based on rough set and evidential reasoning was proposed.First, the information entropy reduction algorithm was used to reduce the customer's driving behavior attributes, and then, the attributes that affected the status of the major components of the automobile significantly were used as evidence.Second, the weight of evidence was measured by the calculation algorithm of attributes importance.Third, the customer's personalized service requirements were inferred by the evidence synthesis algorithm.Finally, the method's effectiveness was verified by the service data of automotive brake system of an automotive service provider from FAW-Volkswagen.Results demonstrate that the rough set method can effectively extract the attributes that have important influence on customer's personalized service requirements from many customer driving behavior attributes as reasoning evidence, the belief degree of the personalized service requirements of all samples can be calculated by using the evidential reasoning method, and the minimum and the average difference between the maximum and the second largest belief degrees are larger than 0.2.These findings indicate that customer's personalized service requirements can be inferred by the method effectively.The proposed method provides a new way for personalized service requirements inference in the filed of automotive service.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".