Author manuscript, published in "ICDM, Vancouver,BC: Canada (2011)" DOI: 10.1109/ICDM.2011.42 Constraint Selection based Semi-supervised Feature Selection
Bibliographic record
Abstract
Dimensionality reduction is a significant task when dealing with high-dimensional data, this reduction can be done by feature selection, which means to select the most appropriate features for data analysis. It is a recent addressed challenge in feature selection research when handling small-labeled with largeunlabeled data sampled from the same population. The supervision information may be used in the form of pairwise constraints; these constraints have practically proven to have very positive effects on the learning performance. Nevertheless, selected constraints sets may have significant results (positive or negative) on learning performance. In this paper, we present a novel feature selection approach based on an efficient selection of pairwise constraints. This aims to grasp the most coherent constraints extracted from labeled party of data. We then evaluate the relevance of a feature according to its 'efficient ' locality preserving and 'chosen ' constraints preserving ability. Finally, experimental results will be provided for validating our proposal in comparison with other known feature selection methods.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.379 | 0.184 |
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".