Improving support vector machine classification accuracy based on kernel parameters optimization
Bibliographic record
Abstract
Support Vector Machine (SVM) learning algorithm is considered as the most popular classification algorithm. It is a supervised learning technique that is mainly based on the conception of decision planes. These decision planes define decision boundaries which are used to separate a set of objects. It is important to extract the main features of the training datasets. These features can be used to define the separation boundaries. The separation boundaries can also be improved by tuning the parameters of the separation hyperplane. In literature, there are different techniques for feature selection and SVM parameters optimization that can be used to improve classification accuracy. There are a wide variety of applications that use SVM classification algorithm, such as text classification, disease diagnosis, gene analysis, and many others.The aim of this paper is to investigate the techniques that can be used to improve the classification accuracy of SVM based on kernel parameters optimization. The datasets are collected from different applications; having different number of classes and different number of features. The analysis and comparison among different kernel parameters were implemented on different datasets to study the effect of the number of features, the number of classes, and kernel parameters on the performance of the classification 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".