An Accurate Model for Text Document Classification Using Machine Learning Techniques
Bibliographic record
Abstract
Text document classification (TDC) is an approach used for the classification of any kind of document for the target category or out.Text classification algorithms have come across significant challenges recently as a result of the exponential expansion of digital text documents; the large volume of words in each document reduces the effectiveness of these existing text classifiers.A key method for improving classification accuracy and getting rid of redundant data is referred to as feature selection (FS).In this work, several phases have been conducted to test and equip the proposed model.Initially, the applied machine learning algorithms were tested and trained using the Reuters-21578 dataset.Second, data cleaning, label encoding, tokenizing, text cleaning, and last TF-IDF vectorization were done to prepare the dataset.Thirdly, four distinct machine learning algorithms, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Random Forest (RF), and Decision Tree (DT) were used to build a brand-new machine learning-based text document classification model (ML-TDCM) for document classification.Finally, several metrics, including F1 score, accuracy, precision, and recall, were used to assess the proposed model.With a 91% classification accuracy, XGBoost turned out to be the best-performing algorithm among the others.The obtained results were also matched with results obtained in past studies, verifying the performance of the suggested models and so defining them as possible methods to be applied in the next work concerning document categorization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".