Commercial video recognition system for short video (TikTok) based on machine learning
Bibliographic record
Abstract
Short video has the features of short duration and high information carrying capacity, which is more in accordance with modern netizens' mobile phone using patterns. With the continual increase of the user scale of smart mobile terminals, many mobile phone users may make full use of the fragmented time to shoot and view short movies. Numerous Internet behemoths are fighting to invest in creating short video platforms since the amount of video user traffic generates enormous commercial prospects. For speeding up the audit team’s effectiveness, video classification technology needs to be constantly developed and updated. The article proposed a commercial video detection model with a wide range of data analysis and processing. More specifically, Principal Component Analysis (PCA), feature selection by random forest and discretization using decision trees would be involved in order to transform the original data into features that better express the nature of the problem. The application of these features to Random Forest Model can improve the model prediction accuracy of data. Experimental results demonstrate that the recognition system fulfills outstanding performance. The model achieves 0.90 precision and 0.96 AUC score (area under ROC curve) of excellent evaluation in the corresponding test set.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".