Ensembles of case‐based reasoning classifiers in high‐dimensional biological domains
Bibliographic record
Abstract
Abstract In order to extend the capabilities of case‐based reasoning (CBR), we implemented an ensemble for case‐based reasoning (E4CBR) approach where an ensemble of CBR classifiers is combined with clustering and feature selection. We first select a subset of features of all the cases, and then cluster the cases into disjoint groups, where each group of cases forms the case‐base of one of the member classifiers. Finally, in each case‐base, a subset of features is ‘locally’ selected individually. To predict the label of an unseen case, each classifier in the ensemble provides a prediction, and the aggregation component of E4CBR combines the predictions by weighing each classifier using a CBR approach—a classifier with more cases similar to the test case receives a higher weight.We evaluated E4CBR on four publicly available biological data sets, and also compared the classification error of E4CBR with a single CBR classifier. In our experiments, we use TA3—a computational framework for CBR systems. Our results show that E4CBR reduces the classification error of our CBR classifier. On the basis of empirical results, our aggregation method outperforms the existing CBR aggregation methods. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 164‐171 DOI: 10.1002/widm.22 This article is categorized under: Algorithmic Development > Ensemble 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".