A New Supervised Term Weight Measure Based Approach for Text Classification
Bibliographic record
Abstract
The textual information is abundantly increasing in the internet through different types of social media platforms. Knowing the type of information is one challenging task to different information retrieval systems and researchers. Text classification is one research domain used to categorize the textual information into different classes. Most of the researchers proposed approaches based on the content used in the textual documents. Identification of appropriate terms for differentiating the text is one important task in text classification. After identification of terms for experiment, next very important task is determining the importance of a term in document representation. The term weight measures are used for finding the importance of a term in a document. In this work, a new supervised term weight measure named as TF-NRF-IPNDF-PNDDF is proposed. The performance of proposed term weight measure is compared with eight popular term weight measures such as TFIDF, TFIEF, TFRF, TF-IDF-ICSDF, TF-PROB, TF-IGM, CDallc and CDc. The experiment conducted on six standard classification datasets such as IMDB, HSS, FN, 20NG, AGN and CBN. Six different classification algorithms such as KNN, NB, LR, SVM, DT and RF are used for evaluating the performance of the proposed term weight measure. The proposed term weight measure attained best accuracies for different standard datasets compared with other term weight measures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".