An efficient ensemble classification method based on novel classifier selection technique
Bibliographic record
Abstract
Individual classification models have recently been challenged by ensemble of classifiers, also known as multiple classifier system, which often shows better classification accuracy. In terms of merging the outputs of an ensemble of classifiers, classifier selection has not attracted as much attention as classifier fusion in the past, mainly because of its higher computational burden. In this paper, we propose a novel technique for improving classifier selection. In our method, the simple divide-and-conquer strategy is adapted in that a complex classification problem is divided into simpler binary sub-classification problems. The proposed ensemble classification technique has the following advantages: f) it requires much less computation than the existing ensemble classification methods. 2) It improves overall classification accuracy. 3) It is also suitable for tackling the classification problems which have a relatively large number of target classes. We conduct extensive experiments on a series of multi-class datasets from the UCI (University of California, Irvine) repository and compare several well-known classification approaches. The experimental results demonstrate the advanced performance of the proposed method.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".