Deep Associative Classifier
Bibliographic record
Abstract
Deep neural networks architecture provides a powerful technique for solving various problems including classification. They owe their performance to the complex and layered data representation and processing built upon neural networks. The success of deep neural networks in various fields has resulted in less focus on other techniques like rule-based models, especially associative classifiers. Associative classifiers are competitive models to deep neural networks on tabular data but suffer from certain limitations i.e., require proper threshold values that differ for different datasets. Even though deep neural networks have resulted in huge success, they have complex and lengthy hyper-parameter tuning. In recent years, attempts to develop models that can compete with deep neural networks using deep representations with decision trees while reducing hyper-parameters have been tried. In this study, we propose a Deep Associative Classifier (DAC), an ensemble of associative classifiers that transforms features in a deep model representation. This model has deep neural network like architecture with associative classifiers as a base learner and overcomes some of the limitations of deep neural network architecture as well as associative classifiers. We use 10 UCI datasets and compare our approach with other existing deep neural network models, a gcForest approach, and associative classifiers. Our proposed model outperforms various state-of-the-art classifiers not only in terms of accuracy but also memory requirement and has fewer hyper-parameters to tune.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".