Boosted Convolutional Decision Trees for Translationally Invariant Pattern Recognition and Transfer Learning
Bibliographic record
Abstract
Decision Tree (DT) models provide a well-known class of interpretable machine learning tools for diverse pattern recognition problems. However, applying DTs to learn floating features in images and categorical data based on their raw representation has been challenging. Convolutional Neural Networks (CNNs) are the current state-of-the-art method for classifying raw images, but have their own disadvantages, including that they are often difficult to interpret, have a large number of parameters and hyperparameters, require a fixed image size, and have only partial translational invariance directly built into its architecture. We propose a novel application of Convolutional Decision Trees (CDTs) and show that our approach is more interpretable and can learn higher quality convolutional filters compared to CNNs. CDTs have full translational invariance built into the architecture and can be trained and make predictions on variable-sized images. Using two independent test cases—protein-DNA binding prediction, and hand-written digit classification—we demonstrate that our GPU-enabled implementation of the Cross Entropy (CE) optimization method for training CDTs learns informative convolutional filters that can both facilitate accurate data classifications in a tree-like pattern and be used for transfer learning to improve CNNs themselves. These results motivate further studies on developing accurate and efficient tree-based models for pattern recognition and computer vision.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".