Shepard Interpolation Neural Networks with K-Means: A Shallow Learning Method for Time Series Classification
Bibliographic record
Abstract
Deep neural network architectures have redefined benchmark machine learning challenges, from classification to anomaly detection, and have become popular in the time series domain. However, deep learning techniques fall short in time series classification (TSC) because the explainability of deep learning is still abstract, and the training requires vast amounts of data, which utilizes computational power. These obstacles are not the case with Shepard Interpolation Neural Networks (SINN), a shallow learning architecture approach for deep learning tasks. Based on a statistical interpolation technique rather than a biological brain, SINN require little data to achieve high accuracy in its training. Additionally, its explainability can be equated to feature mapping onto hyper surfaces in the feature space. Our proposed algorithm outperforms the other state-of-the-art algorithms on the popular UCR time series classification benchmark data set and outperforms LSTMs on data sets which have significantly smaller training data than testing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".