Bias Estimation and Correction in a Classifier using Product of Likelihood-Gaussians
Bibliographic record
Abstract
In any classification task the confusion error, in general, is proportional to the number of classes. This is mainly due to sharing of some common attributes (feature vectors) among different classes. This, in many cases, leads to a serious problem, in the sense that, the classifier itself may be biased towards a specific class or a subset of classes. An ideal classifier is not expected to have any such bias. If we assume that, for a given pair of models and their corresponding training data, the log-likelihoods are distributed normally, the bias of any of these models may be visualized in the likelihood-space as an overlap between Gaussian likelihoods of different models (classes). In this paper, we propose a discriminant measure, using a product of Gaussian likelihoods, to estimate the amount of bias. By adjusting the complexity of the models, we show that this bias can be neutralized and a better classification accuracy can be achieved. Presently, the experiments are carried out on the OGLMLTS telephone speech corpus on a language identification task. The results show that a better classification accuracy can be achieved without any degradation in the performance of any of the individual classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.037 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".