An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Typically, in sparse representation‐based classifiers, the weight associated with each training sample is ignored, resulting in reduced accuracy. Moreover, individual binary classifiers solved a multiclass problem. It requires more time as multiple runs are needed to compute the accuracy. In this paper, we propose a novel optimal sparse representation‐based classifier. It solves the ternary classification problem with improved accuracy in a single run. The ternary classification considers Alzheimer's disease versus mild cognitive impairment versus normal control in a single run. A two‐stage sparse representation model is used to design the proposed classifier. To update the weight coefficients, we suggest a regularized Levenberg–Marquardt learning. It allows selecting a subset of significant training samples. To determine the appropriate subset size, we investigate an objective function in terms of classification accuracy. For optimization, we suggest a hybrid particle swarm optimization–squirrel search technique. The experiment conducted on the Alzheimer's Disease Neuroimaging Initiative database shows our method outperforms other state‐of‐the‐art methods in terms of computation time and accuracy. The use of different training–testing partition ratios makes the proposed method immune to biased results, overfitting, and underfitting difficulties. Moreover, results are obtained from 100 iterations to confirm its stability. The suggested model may be helpful for further research in medical image analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it