An Application Of Machine Learning Methods On Complicated Large Scale Dataset
Bibliographic record
Abstract
Machine learning research has been an upcoming trend over the last few years. With more computational power and increasing volume of data available thanks to the development of the Internet, machine learning methods could be applied to real life problems and produce fascinating outcomes. Furthermore, with the rise of deep learning methodologies, machine learning practitioners can work on unstructured datasets and achieve human level accuracy. The present thesis focuses on a structured dataset with unstructured fields and information, aiming to apply multiple machine learning methods from a supervised learning perspective. Firstly, linear regression models and extreme gradient boosting machine, as conventional machine learning methods, are applied on certain selected features of the dataset, achieving remarkable results. They serve as base models. Next, an ensembled neural network model with four modules, i.e., fully connected module, embedding module, convolutional network module, and recurrent network module, is created and implemented with the transfer learning technique, which yields better outcomes. This work leads to a typical supervised learning structure and can be considered as a stepping stone for similar practical implementations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".