Developing expert system on decision making unit efficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it