Multi-objective Evolutionary Optimization of Neural Networks for Virtual Reality Visual Data Mining: Application to Hydrochemistry
Why this work is in the frame
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Bibliographic record
Abstract
A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on neural networks is presented. Two neural network layers (output and last hidden) are used for the construction of simultaneous solutions for: a supervised classification of data patterns and the computation of two unsupervised similarity structure preservation measures between the original data matrix and its image in the new space. A set of spaces is constructed from selected solutions along the Pareto front which enables the understanding of the internal properties of the data based on visual inspection of non-dominating spaces with different properties. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. The presented approach is domain independent and is illustrated with an application to the study of hydrochemical properties of ice and water samples from the Arctic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it