A computational experience for automatic feature selection on big data frameworks
Bibliographic record
Abstract
The classification rule system is one of the predictive analytical techniques used in Big Data problems, where finding datasets with millions of rows but also with dozens of variables (attributes) is common.Classification rule systems consist of rule sets which have a so-called antecedent (variable or set of variables that can be numeric or nominal) and a consequent (target variable, provided nominal).If the antecedent variables are numerical, many generator algorithms of classification rules employ traditional methods of automatic feature selection, based on techniques already established in the scientific field, such as discriminant analysis or cluster analysis.In this paper, the authors propose the comparison of their own method of feature selection and classification, RBS (originally designed to manage only nominal variables) and classical methods of feature selection.After the formal definition of our own method, this paper presents the design of a computing experience that allows a qualitative and quantitative comparison of the adapted RBS and other methods for feature selection.Finally, optimal conditions of application of each method are discussed and future research areas in the field of automatic feature selection are identified.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".