COR-MFS: A Correlation-Based Multi-Objective Feature Selection on EEG Signals
Bibliographic record
Abstract
Feature selection is a crucial step in the model-building pipeline in machine learning (ML) applications such as Electroencephalogram (EEG) signal processing, providing benefits on model performance and computational efficiency. EEG signals play a pivotal role in elucidating human nature through promising ML mechanisms. However, extracting a large number of features from the EEG signals can be a challenge of efficient EEG processing. A multi-objective feature selection strategy applied to the extracted features from EEG signals can simultaneously improve the accuracy of the process and reduce the number of features. However, the high dimensionality of the EEG feature vectors actually diminishes the exploration capabilities of multi-objective algorithms, impeding their real-world applicability. Hence, we propose a novel correlation-based multi-objective feature selection (COR-MFS) method, that aims to reduce dimensionality before applying the multi-objective algorithm. In the initial phase, a correlation-based dimension re-duction method is applied to filter the most relevant features with highest correlation with the class label. Subsequently, a multi-objective feature selection algorithm is applied to the shrunk search space enhancing optimization efficiency and facilitating the search process. We evaluated the proposed COR-MFS method using six large-scale EEG datasets and observed significant improvements compared to the stand-alone multi-objective feature selection. This underscores the effectiveness of our innovative framework in providing more accurate solutions with fewer number of features for extensive EEG-based classification tasks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".