Modified K-Means Clustering Algorithms for Feature Selection
Bibliographic record
Abstract
Computational effort is difficult when dealing with high dimensional data that has hundreds or thousands of features. Features that don't significantly influence class predictions throughout the classification process increase the computing load. By eliminating unnecessary, redundant, or noisy features from the original features, feature selection, as a dimensionality reduction strategy, tries to pick a small subset of the important features from the original features. Two new feature selection methods are described in this study in relation to the effectiveness of k-means-based clustering methods. This research project aims to reduce the number of different features by clustering the D features into k (k < D) clusters, determining the cluster center to represent its members by finding the closest feature to the cluster center or selecting the highest weighted features among the cluster members, and performing feature selection. After removing 41.4% of the features from the VIRUS-MNIST dataset, we are able to deliver accuracy equivalent to the original dataset using both of our suggested methods in a shorter amount of time. Our proposed methods outperform sparse k-means, PCA, LLE, and wk-means-based feature selection method for clustering by ANN following feature reduction in the Wine dataset. With fewer features than the modified k-means feature selection method, our second method performs more accurately on the CNAE dataset.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".