Feature Selection for Improving Case-Based Classifiers on High-Dimensional Data Sets.
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
Case-based reasoning (CBR) is a suitable paradigm for class discovery in molecular biology, where the rules that define the domain knowledge are difficult to obtain, and there is not suf-ficient knowledge for formal knowledge representation. To extend the capabilities of this paradigm, we propose logistic regression for CBR (LR4CBR), a method that uses logistic regression as a feature selection (FS) method for CBR sys-tems. Our method not only improves the prediction accuracy of CBR classifiers in biomedical domains, but also selects a subset of features that have meaningful relationships with their class labels. In this paper, we introduce two methods to rank features for logistic regression. We show that using logistic regression as a filter FS method outperforms other FS techniques, such as Fisher and t-test, which have been widely used in analyzing biological data sets. The FS methods are combined with a computational framework for a CBR system called TA3. We also evaluate the method on two mass spectrometry data sets, and show that the prediction accuracy of TA3 improves from 90 % to 98 % and from 79.2 % to 95.4%. Finally, we compare our list of discovered biomarkers with the lists of selected biomarkers from other studies for the mass spectrometry data sets, and show the overlapping biomarkers.
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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 | 0.001 | 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