Ensemble Methods for ECG-Based Heartbeat Classification
Why this work is in the frame
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Bibliographic record
Abstract
Ensemble method is a meta-algorithm to build strong classifiers based on a set of weak classifiers. This work explores some ensemble classifiers on UCI Arrhythmia Dataset to classify the heartbeat records. Each record corresponds to a person, and is the features extracted from the person's raw ECG data over a period. Two popular ensemble classifiers XGBoost and RandomForest are used, and the classic LogisticRegression is tried as a comparison. These classifiers are applied on all the 279 features in the dataset, and predict the heartbeat categories for the records. XGBoost and RandomForest perform better than even well-tuned LogisticRegression. We build VotingClassifier based on the ensemble voting metaalgorithm and the above three built-in classifiers, and it outperforms even welltuned XGBoost and RandomForest. The best prediction accuracy 76% is achieved by the VotingClassifier in this multiclass classification problem. This result is comparable to many other findings that uses similar or different classifiers.
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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 | 0.001 | 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 it