Intelligent Decision Making Technique for Marketing Using Hypothetical Database and Fuzzy Multi-Criteria Method
Bibliographic record
Abstract
Marketing organizations use databases to locate potential customers and to generate sales lead. A number of software systems have been playing a key role in supporting the decision making activities in recent years but common problem in all that they cannot handle fuzzy data appropriately for "what-if" analysis. This paper proposes intelligent scenarios analysis system framework for marketing decision support which deals with crisp and fuzzy data like linguistic variable. Applying new approach "Hypothetical Database" for derived data that permits decision manager to manage views according to the need of organization and/or market environment. View in Hypothetical Database provides versatility in "What-If" analysis by using versions of "What-If" database and reduce data redundancy and data storage in updating. Using Fuzzy database will help to handle imprecise and uncertain information like "Linguistic variable" in a more human oriented process. Finally, the projected scenarios selected by decision manager will be aligned in a hierarchy according to the distance from "Ideal Vector" by using Fuzzy Multi-Criteria Decision Making method.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".