Extended fast feature selection for classification modeling
Why this work is in the frame
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Bibliographic record
Abstract
The performance of a classification algorithm in data mining is greatly affected by the quality of data source. Irrelevant and redundant features of data not only increase the cost of mining process, but also degrade the quality of the result in some cases. This issue is particularly important to high-dimensional data, in that many features may either irrelevant or redundant for a selected classification target. Accordingly, feature selection becomes an essential part in data preparation. The feature selection for classification is to identify and remove irrelevant and redundant features, which do not contribute to modeling for a selected target. Among the existing feature selection methods, fast correlation-based filter and correlation-based feature selection are most commonly used approaches. The main concern of the these methods is that they may over simplify the features of a given data set by removing many useful features because of certain inherent limitation in these methods. As a result, the selected feature set may be over-simplified to be useful in practice. In this paper, we analyze the existing issue, and present an extended fast feature selection algorithm to overcome the problem. Experiments are conducted using real data from financial institutions to demonstrate the improvement in terms of quality of selected features. A result comparison between the proposed method and other three major methods is provided.
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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