Generalized group sparse classifiers with application in fMRI brain decoding
Bibliographic record
Abstract
The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic resonance imaging (fMRI) classification. In this paper, we present a novel formulation for constructing “Generalized Group Sparse Classifiers” (GSSC) to alleviate these problems. In particular, we propose an extension of group LASSO that permits associations between features within (predefined) groups to be modeled. Integrating this new penalty into classifier learning enables incorporation of additional prior information beyond group structure. In the context of fMRI, GGSC provides a flexible means for modeling how the brain is functionally organized into specialized modules (i.e. groups of voxels) with spatially proximal voxels often displaying similar level of brain activity (i.e. feature associations). Applying GSSC to real fMRI data improved predictive performance over standard classifiers, while providing more neurologically interpretable classifier weight patterns. Our results thus demonstrate the importance of incorporating prior knowledge into classification problems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".