Providing a Combination Classification (Honeybee Clooney and Decision Tree) Based on Developmental Learning
Bibliographic record
Abstract
<p class="zhengwen">The aim of this study is to provide a combination classification based on developmental learning in the proposed method using algorithms inspired by nature (honeybee Clooney) and decision tree, by using algorithm classifier consensus is proposed that this method, at first classifier once implemented and based on the detection rate of input data agreement in the final consensus which is an innovation in this research. To implement the proposed algorithms used MATLAB software. Note that, this is an increase compared to the classifiers ensemble, it have accuracy and fix. This shows that this method of making the ensemble by helping bee Clooney algorithm, when appropriate and effective which the number of data collection records is high or the number of study characteristics is high. In this study, we proposed algorithm on 8 samples tested. However, training time of this method compared with simple ensemble is a slower process but this method compared with simple ensemble method has higher accuracy, this shows, if we want a higher accuracy, we should be spent more time.</p>In general, if the accuracy of the process have a large importance for us, this method can be a good option to get the results that almost optimal.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".