A Recognition-based Alternative to Discrimination-based Perceptrons
Bibliographic record
Abstract
Though impressive classification accuracy is often obtained via discrimination-based learning techniques such as Multi-Layer Perceptrons (DMLP), these techniques often assume that the underlying training sets are optimally balanced (in terms of the number of positive and negative examples). Unfortunately, this is not always the case. In this paper, we look at a recognitionbased approach whose accuracy in such environments is superior to that obtained via more conventional mechanisms. At the heart of the new technique is a modified auto-encoder that allows for the incorporation of a recognition component into the conventional MLP mechanism. In short, rather than being associated with an output value of 1, positive examples are fully reconstructed at the network output layer while negative examples, rather than being associated with an output value of 0, have their inverse derived at the output layer. The result is an auto-encoder able to recognize positive examples while discriminating against negative ones by virtue of the fact that negative cases generate larger reconstruction errors. A simple technique is employed to exaggerate the impact of training with these negative examples so that reconstruction errors can be more reliably established. Preliminary testing on both seismic and sonar data sets has demonstrated that the new method produces lower error rates than standard connectionist systems in imbalanced settings. Our approach thus suggests a simple and more robust alternative to commonly used classification mechanisms.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".