Comparing "pattern discovery" and back-propagation classifiers
Bibliographic record
Abstract
The pattern discovery (PD) algorithm of Wang and Wong was applied as a classifier to several continuous-valued data sets generated to explore performance across a selection of interesting linearly and non-linearly separable class distributions. Performance of several configurations of PD and backpropagation (BP) neural network classifiers and a minimum inter-class distance (MICD) classifier was quantified and compared. The best performance of the PD and BP classifiers were found to be similar for all class distributions studied and close to the optimal IMICD performance for linearly separable class distributions. The performance of both PD and BP classifiers was dependent on the classifier configuration. PD classifier performance depended on the number of intervals used to quantize the continuous data in a predictable, class-distribution independent way. BP performance depended on the number of hidden nodes in a way which was class-distribution dependent and difficult to determine a priori. The transparency and statistical validity of the patterns used and the decisions made by PD classifiers make them highly suitable for problems in which the rationale and confidence of classifications are required so that multiple classifications can be effectively combined to support decisions in a broader context such as medical diagnosis. The strong absolute and relative performance of PD classifiers and the relative simplicity of their implementation when applied to continuous-valued data suggest that they can be effectively utilized in decision support systems in which the underlying data is continuous or discrete valued.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".