Cost-sensitive classification on class-balanced ensembles for imbalanced non-coding RNA data
Bibliographic record
Abstract
Many bioinformatics data sets have class-imbalanced data, where the number of samples in each class is not equal. Since most of data sets contain usual versus unusual cases, e.g. cancer versus normal or miRNAs versus other non-coding RNA, where the minority class with the least number of samples is the interesting class that contains the unusual cases. The learning models based on the standard classifiers, such as the support vector machine (SVM), random forest and k-NN are usually biased towards the majority class, which means that the classifier is most likely to predict the samples from the interesting class inaccurately. Thus, handling class-imbalanced data set has gained the researchers interests recently. A combination of proper feature selection, a cost-sensitive classifier and ensembling based on random forest method (BCE-CSC-RF) is proposed to handle the class-imbalanced data. Random class-balanced ensembles are built individually. Then, each ensemble is used as a training pool to classify the rest of out-bagged samples. Samples in each ensemble will be classified using class-sensitive classifier that incorporates random forest. The sample will be classified by selecting the most often class has been voted-for in all samples appearances in all the formed ensembles. A set of performance measurements including a geometric measurement suggests that the model can improve the classification of the minority class samples.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".