Cluster Analysis and Visualization Enhanced Genetic Algorithm——II. Analysis of Cases and Validation
Bibliographic record
Abstract
This paper validated that the Cluster Constrained Mapping (CCM) can keep the 搕opological?information of the points in the reduced dimension map by comparing the cluster results obtained using the K-means algorithmThe enhanced GA proposed in Part I was applied to three constrained optimization cases. The results show that the combination of visualization, cluster analysis and genetic algorithms can help users to participate in selectingappropriate parameters of clusters, and the combination of a computer and the user is more powerful than eitheralone, which is an effective process optimal design tool with high solution quality and consistency. In the new cluster analysis method, the data are visualized by CCM that provides immediate direct information about the feasibledomain, and the user is directly involved in determining the parameters for the cluster analysis and increasing theeffectiveness of feasible regions discovery by visual interaction; the obtained knowledge is visualized by ParallelCoordinate Systems (PCS), thus the user has a deeper understanding of the feasible regions. It is clear that in most cases the proposed IGA based on the combination of visualization and cluster analysis has performed not only with the high efficiency (in terms of getting closer to the best-known solution) and with more robustness (in terms of the number of GA runs finding solutions close to the best known solution), but also with providing more information about the feasible regions for the user to understand the model and accept the optimal results.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".