DAMP: Decision-Making with the Combination of Analytical Hierarchy Process and Deep Learning (Case study: Car Sales Forecasting)
Bibliographic record
Abstract
Nowadays, prediction and decision making are two inseparable principles in the management and two distinct roles of managers. The organizations spend a large part of their budgets on predictions from past data. They will lose their money if they are neglected. On the other hand, decision-making is the most critical step in problem-solving. Moreover, it is considered the main task of a manager as a problem solver. Making decision becomes more complicated when we are faced with multi-criteria decision-making issues. Combining prediction and decision-making approaches helps researchers to make a better choice utilizing prior knowledge. One of the most essential and comprehensive systems designed for multi-criteria decision-making is Analytical Hierarchy Process (AHP) process. Deep learning as a valuable extension of artificial neural networks has been the focus of many researchers. In this paper, AHP is used to classify, compare, and determine the weights of a deep learning approach. In order to evaluate the efficiency of the proposed method, the prediction of vehicle price application is chosen, and the results are compared with neural networks. The data set is related to the sale of Hyundai and Kia Motors cars in the United States and Canada. It is emphasized that the data are used only to evaluate the proposed method and can be generalized to solve all similar issues. The sales forecasting data of two car companies showed that the proposed method is superior to other regression methods. To extend the proposed methos as our future work, the aim will be to develop a comprehensive decision-making and forecasting system by combining these two approaches.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".