Effects of Feature Selection on Machine Learning-Based Outdoor Insulator Defects Classification
Bibliographic record
Abstract
In this study, acoustic signals obtained from a $138 / 13.8 \mathrm{kV}(60 \mathrm{~Hz})$ substation using a Sonaphone BS30 acoustic sensor were analyzed. The recorded signals for three different classes (healthy, dry band arcing and loose connection) have been divided into training and test data and divided into 0.0167second windows. A total of 40 features have been extracted from each window in the time, frequency, and time-frequency domains. Z-score normalization, L2 normalization, and robust scaling methods have been applied to the obtained features. Subsequently, all features, along with selected subsets of features using the ReliefF, Neighbourhood Components Analysis, and BChimp1 algorithms, have been tested separately with kNN, SVM, and Naive Bayes classifiers. The results clearly demonstrated the effects of preprocessing and feature selection on classification performance. Overall, the kNN and SVM classifiers had higher accuracy rates, while the Neighbourhood Components Analysis algorithm achieved the highest accuracy despite selecting fewer features.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".