A Novel Method for Text Classification Dealing with Local Data Structures and High Data Dimensionality
Bibliographic record
Abstract
Text categorization involves training models and making predictions over high-dimensional feature space. With the exponential growth on the amount of text resources on the Internet, the demand and difficulty of classifying digital text documents increases over the past decades. k nearest neighbors (kNN) has been proved to possess satisfying predictive power on text classification problems. However, kNN is sensitive to local data structures and its time cost is proportional to the product of the training set size and the feature space dimensionality. This paper proposes a novel text classification method called PCA pre-processed local correlation vector kNN (PCA-Local-CorVec-kNN) that applies PCA and kNN consecutively to the text datasets and introduces local correlation vectors to the sets of nearest neighbors produced by kNN. The proposed method has lower time cost than conventional kNN because of using PCA and is robust to local data structures since correlation vectors are introduced. Problems such as imbalanced datasets, choosing different k values for different samples, weighted voting are also tackled by the proposed method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".