Meta-Feature Based Data Mining Service Selection and Recommendation Using Machine Learning Models
Bibliographic record
Abstract
Quality of Service (QoS) based web service selection has been studied in the service computing community for some time. However, characteristics of the input dataset are not usually considered in the selection process, even though they might have an impact on the QoS values of the service. To address this issue, we propose a QoS-based service selection process that considers the impact of dataset features and we focus on data mining services because their QoS values could be highly dependent on dataset features. We have used a meta-learning algorithm to incorporate dataset features in the selection process and studied the use of different machine learning algorithms (both classification models and regression models) as meta-learners in recommending data mining services for the given dataset. We have also investigated the impact of the number of dataset features on the performance of the meta-learners. Out of the five classification models examined here, Support Vector Machine (SVM) showed the best results in predicting the recommended service for the given dataset with an accuracy of 78%. When it comes to regression models, Multilayer Perceptron (MLP) was the best regressor. We recommend considering only simple meta-features that can be collected for most datasets, as those proved to be sufficient to achieve good prediction accuracy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".