Evaluating Performances of Two Unsupervised Methods for Classification Crime Text
Bibliographic record
Abstract
Crime is a pervasive societal issue that has a negative impact on both a community's economic growth and overall quality of life.The bulk of crimes committed in everyday times are documented online by the wherewithal of news reports, blogs, and social networking sites.To improve crime analytics and community protection in response to rising crime.Law enforcement agencies continue to promote effective electronic information systems and crime data mining.Consequently, the aim of this study is to design a system of crime that depends on unsupervised machine-learning techniques that categorize five types of crime text.Two famous unsupervised algorithms: Independent Component Analysis based on natural gradient (NG-ICA) and Fast Independent Component Analysis (Fast-ICA) were used, to recover the latent components from observations.In order to evaluate the proposed system, the BERNAMA dataset, which had been manually annotated was used.Two experiments were conducted, and the results showed that the approaches that were employed satisfied promising results.Where the NG-ICA achieved an average F-measure of 83.3% and the Fast-ICA achieved 87.1%.This outcome demonstrates the appropriateness of these techniques in the implementation of text mining.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".