A Novel Online QoE Prediction Model Based on Multiclass Incremental Support Vector Machine
Bibliographic record
Abstract
Satisfying the user it's a primary goal to reach by telecom operators. Therefore, Quality of Experience (QoE), which is the measure of the user-perceived quality of a received service, has become a pivotal topic in the academic research. Generally, an efficient QoE model should be able to handle dynamic environments with large scale data, in order to continuously acquire feedback from the user, and then provide a real-time and accurate description of his perception. This paper proposes a novel online QoE estimation model, which is able to classify user perception toward video streaming service, using incremental multiclass SVM (multiclass-iSVM). The proposed online QoE model investigates the effectiveness of incremental learning, in order to handle large scale dynamic data and to improve prediction accuracy of QoE. In fact, it uses the mathematical properties of SVM and updates its unknown weights, as well as, the classification results incrementally, as new observations are considered. Comparative evaluation of the proposed multiclass iSVM-based QoE model is performed to show its superiority over relevant batch learning based models, in terms of QoE prediction accuracy and computational complexity. In particular, this model has achieved the highest classification rate of 89%, starting with only 10% of the dataset at the beginning of the incremental process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".