A Comparison of Error Correcting Output Coding Methods for Multiclass Classification by Using Support Vector Machine: The Prediction of Self-Monitoring of Blood Sugar
Bibliographic record
Abstract
SVMs were initially developed to perform binary classification. However, in many real-world problems, particularly pattern recocnation studies, aimed to determine the distinctive features of large number of class or group. For this reason, a number of methods to generate multiclass SVMs from binary SVMs have been proposed by researchers and this is still a continuing research topic. In this study we aimed to compare classification accuracy and computational cost of four multiclass approaches using a original and simulated data sets. Error Corrected Output Coding (ECOC) based multiclass approaches that is used in this study creates many binary classifiers and combines their results to determine the class label of a test pixel. As a result of the comparisons for all conditions examined in this study, it’s found that the classification accuracy and computational cost of One vs. One multiclass approach is better than the other multiclass approaches. In classification or pattern recognization problems, some of supervised machine learning methods or algorithms can be easily extended to multiclass problems. However, some other powerful and popular classifiers, such as AdaBoost and Support Vector Machines, do not extend to multiclass easily. In those situations, the usual way to proceed is to reduce the complexity of the multiclass problem into multiple simpler binary classification problems.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".