Developing expert system on decision making unit efficiency
Bibliographic record
Abstract
Efficiency is a key concept for financial institutions. As personnel specifications have greatest impact on efficiency, they can help us designing work environments for maximizing efficiency. Providing information on multiple input and output factors are a complicated and time consuming procedure. Developing expert system in this situation is hard. This paper proposed a procedure that solved mentioned problem. At first, the integrated approach determining important attributes and then expert system is developed. The integrated approach uses Data Envelopment Analysis (DEA) and Data Mining tools. DEA is used for DMUs efficiency evaluation. Artificial Neural Network (ANN) and Cross Validation Test Technique (CVTT) are used for precision testing and forecasting and finally DEA is again utilized for identification of attributes importance. ANN is used for determining important attributes and developing expert system. As well, K-means algorithm is used in developing expert system. A Procedure is proposed to developing expert system with mentioned tools and completed rule base. The constructed expert system helps managers to forecast DMUs efficiencies by selected attributes and grouping inferred efficiency. Also, they can assess new situation before happening and compare with present situation. The proposed integrated approach is applied to an actual banking system and its superiorities and advantages are discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".