A choquet integral-based multi-class classifier and its applications on the prediction of membrane protein types
Bibliographic record
Abstract
In this paper, a novel aggregator information-based strategy for predicting membrane proteins types is introduced. In particular, we propose a framework of five Choquet Integrals (one Choquet Integral for each protein type) that are specialized to compute the global score of each class of proteins. These global scores are obtained by the combination of the partial evaluations of several membrane protein features provided by different individual classifiers. To compute the fuzzy measures associated with each Choquet Integral, we use a new unsupervised method (International Journal of Intelligent Systems, January 2008) proposed in the literature in which the concept of importance of attributes (in our case, the importance of the subsets of the classifiers) is replaced by that of information content in the subsets of classifiers. The parameters of the individual classifiers are adjusted with a conventional training dataset of 2059 sequences of aminoacids where 435 are Type I, 152 Type II, 1311 are multipass trans-membrane, 51 lipid-chain-anchored and 110 GPI-anchored type. The results obtained in this experiment, shows that our proposed method obtains a higher classification accuracy compared with the results obtained for several methods cited in the literature.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".