Learning with Limited Datasets: From Deep-Learning to Traditional Machine-Learning
Bibliographic record
Abstract
Learning with datasets containing a limited number of exemplars is a contentious research area. Researchers have used deep learning (DL) models with a large number of trainable parameters for such limited datasets leading to problems such as overfitting, over-parameterization, lack of generalization, and the need for large computational resources. Judicious use of appropriate learning methodologies may be in order when the dataset for training is limited. Non-DL methodologies or traditional machine learning methodologies with appropriate pre-processing and feature extraction techniques may perform at par or better than DL techniques for applications that have a limited dataset. This dissertation aims to establish this proposition for two healthcare applications namely, radar-based monitoring of human activities (and fall event detection) and thermography-based breast abnormality detection by developing computationally inexpensive novel supervised and unsupervised non-DL learning methodologies for binary and multi-class classification problems that outperform the current state-of-the-art techniques of the respective fields in those healthcare applications. The developed learning methodologies use traditional machine learning classifiers along with interpretable hand-crafted features such as histograms of oriented gradient (HOG), statistical features, and textural features. These novel learning methodologies use ensemble learning approaches such as early fusion, intermediate fusion, decision fusion, or training error correction. Novel contrast enhancement and novel gradient enhancement methodologies using binary encodings such as census transform and local binary patterns are also proposed to improve classification performance. For both applications, learning in the compressed domain using deterministic compressive sensing is introduced to reduce the number of trainable parameters of the developed novel supervised non-DL methodologies. The novel supervised learning methodologies using hand-crafted features achieved an average accuracy of 98% and 96% for fall event detection and breast abnormality detection, respectively. The novel unsupervised learning methodologies using hand-crafted features achieved an average error rate of 1.1% for fall event detection and an average F1-score of 85% for breast abnormality detection. At 0.875 compression ratio, the novel supervised learning methodologies in the compressed domain achieved an average accuracy of 97% and 87% for fall event detection and breast abnormality detection, respectively.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".