An LDA-SVM Active Learning Framework for Web Service Classification
Bibliographic record
Abstract
Classifying Web services and labeling them based on their functional features have played a major role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. Existing approaches leverage text mining techniques and follow a supervised learning process, which involves building a classifier from a training set of services and applying the classifier to other services. This process requires intensive human effort on labeling services in the training set. In this paper, we propose to leverage the idea of pool-based active learning to realize a scalable service classification approach. Instead of manually labeling a large number of services to construct a complete training set, the approach starts with a base classifier with a small set of training set and iteratively asks for the labels of the most informative services outside of the initial training set. By doing this, the classifier can achieve comparable accuracy compared to traditional classification method with much smaller size of training set. We use SVM as the base classifier due to its effectiveness in text classification. We also incorporate probabilistic topic models to address the issues caused by sparse term vectors generated from service descriptions and reduce the dimensions to improve the efficiency. We conducted a comprehensive experimental study on real-world service data to demonstrate the effectiveness of the proposed approach.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".