Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features
Bibliographic record
Abstract
In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered “Sequential Forward Selection” for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis Optimization algorithm (TOA). We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".