A Comprehensive Machine Learning Framework for Automated Book Genre Classifier
Bibliographic record
Abstract
Machine learning has been leveraged in the digital era, resulting in an increasing desire for computers to perform human-like tasks.Text classification is rapidly becoming one of the most significant applications of machine learning.However, the manual reading and classification of books based on genre requires substantial time and effort.As a result, machine learning methods are critical for enabling automated classification.In this study, a book description-based text classification framework was proposed, utilizing a wealth of information about book contents.The automated classification of books was achieved through the implementation of supervised machine learning.A variety of classifiers were employed, including Multinomial Naive Bayes, Gradient Boosting, and Random Forest, to categorize book genres.According to the results, the Naive Bayes classifier outperformed the other two techniques in classification accuracy, while comparable performance was achieved with Gradient Boosting and Random Forest.The comprehensive machine learning framework efficiently and accurately categorized books by extracting information from book descriptions.The proposed methodology has the potential to facilitate large-scale book classification for both academic and industrial purposes.Overall, this study provided an automated solution to relieve the burden of manual classification while achieving high accuracy.
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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".