Virtual reality spaces: visual data mining with a hybrid computational intelligence tool
Bibliographic record
Abstract
The information explosion requires the development of alternative data mining procedures that speed up the process of scientific discovery. The improved in-depth understanding and ease of interpretability of the internal structure of data by investigators allows focusing on the most important issues, which is crucial for the identification of valid, novel, potentially useful, and understandable patterns (regularities, oddities, surprises, etc). Computational visualization techniques are used to explore, in an immersive fashion, inherent data structure in both an unsupervised and supervised manner. Supervision is provided via i) domain knowledge contained in the data, and ii) unsupervised data mining procedures, such as fuzzy clustering, etc. The Virtual Reality (VR) approach for large heterogeneous, incomplete and imprecise (fuzzy) information is introduced for the problem of visualizing and analyzing general forms of data. The method is based on mappings between a heterogeneous space representing the data, and a homogeneous virtual reality space. This VR-based visual data mining technique allows the incorporation of the unmatched geometric capabilities of the human brain into the knowledge discovery process. Traditional means of interpretation would require more time and effort in order to achieve the same level of deep understanding of complex high dimensional data as the proposed technique. This hybrid approach has been applied successfully to a wide variety of real-world domains including astronomy, genomics, and geology, providing useful insights.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".