Multi Chaotic Flow Direction Algorithm for Feature Selection
Bibliographic record
Abstract
Feature selection is a crucial pre-processing step used to remove redundant information from original datasets while preserving the accuracy and processing time of classifier.The feasibility of using metaheuristic search algorithms (MSAs) such as Flow Directional Algorithm (FDA) to solve feature selection problems is one of the active research topics.Similar with other MSAs, FDA also employs conventional initialization scheme that generates initial solutions in random basis.The absence of intelligent mechanisms in conventional initialize scheme tends to generate initial populations in local optima, hence compromising the performance of algorithm to handle datasets with complex features.In this paper, a modified algorithm known as Multi Chaotic Flow Directional Algorithm (MCFDA) is proposed to solve feature selection problems with enhanced performances by leveraging the strengths of multiple chaotic maps for population initialization.A total of 12 datasets from UCI Machine Learning Repository are selected for performance evaluation of MCFDA and another four peer algorithms to solve feature selection problems.The proposed MCFFA is revealed to deliver best performances by solving 7 out of 12 datasets with the best mean classification accuracy and 6 out of 12 datasets with the least numbers of selected features.
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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".