Feature Selection Methods for Multiphase Reactors Data Classification
Bibliographic record
Abstract
The design of reliable data-driven classifiers able to predict flow regimes in trickle beds or bed initial behavior (contraction/expansion) in three-phase fluidized beds requires as a first step the identification of a restrained number of salient variables among all the numerous available features. Reduction of dimensionality of the feature space is urged by the fact that lesser training samples may be required and/or more reliable estimates for the classifier parameters may be achieved and/or improvement in accuracy can be achieved. This work investigates several methodologies to identify the relevant features in two classification problems belonging to a multiphase reactor context. Relevance of the subsets was assessed using mutual information between the subsets and the class variable (filter approach) and by the accuracy rate of a one-nearest neighbor classifier (wrapper approach). Algorithms for generating feasible sets to maximize these relevance criteria that were investigated were the sequential forward selection and the plus- l -take away r . Another conceptually different method to feature ranking that was tested was based on the Garson's saliency indices derived from the weights of classification neural networks. Reliability of the feature selection methodologies was first evaluated on two benchmark problems (a synthetic problem and the Anderson's iris data). They were henceforth applied to the two multiphase reactors classification problems with the goal of identifying the most appropriate features subsets to be used into classifiers. Finally, a new feature selection algorithm which combines filter and wrapper techniques proved to yield the same solutions as the wrapper technique while being less computationally expensive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".