An Empirical Analysis of Imbalanced Data Classification
Bibliographic record
Abstract
SVM has been given top consideration for addressing the challenging problem of data imbalance learning. Here,we conduct an empirical classification analysis of new UCI datasets that have dierent imbalance ratios, sizes andcomplexities. The experimentation consists of comparing the classification results of SVM with two other popularclassifiers, Naive Bayes and decision tree C4.5, to explore their pros and cons. To make the comparative exper-iments more comprehensive and have a better idea about the learning performance of each classifier, we employin total four performance metrics: Sensitive, Specificity, G-means and time-based eciency. For each benchmarkdataset, we perform an empirical search of the learning model through numerous training of the three classifiersunder dierent parameter settings and performance measurements. This paper exposes the most significant resultsi.e. the highest performance achieved by each classifier for each dataset. In summary, SVM outperforms the othertwo classifiers in terms of Sensitive (or Specificity) for all the datasets, and is more accurate in terms of G-meanswhen classifying large datasets.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".