Research on Feature Extraction and Classification for Unstructured Data Based on Deep Learning
Bibliographic record
Abstract
With the rapid growth of unstructured data such as text, images, audio, and video, traditional data analysis techniques are facing great challenges in dealing with the complexity and high dimensionality of such data. In this study, we propose a multimodal enhanced Transformer model to process and fuse different types of unstructured data by improving the self-attention mechanism and designing a multi-stream input architecture. Firstly, the model adopts a multi-stream input structure, each stream processes a data modality separately, and maps the data of each modality to the feature space of the same dimension through a dedicated preprocessing and coding network to form a unified representation. Subsequently, a cross-modal self-attention mechanism is introduced into the model, which can establish a global dependency between different modalities and automatically learn the correlation between modal features, so as to extract key features more accurately in the classification process. In order to reduce the computational complexity, an optimization algorithm based on sparse matrix is used to enable the model to efficiently process long sequences and high-dimensional data. Experimental analysis on the benchmark dataset shows that the proposed model is superior to the existing methods in terms of accuracy, precision and recall.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".