Multi-Objective Hierarchical Classification Using Wearable Sensors in a Health Application
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper introduces a novel multi-classification technique, which improves two conflicting main objectives of classification problems, i.e., classification accuracy and worst case sensitivity. Global performance measures such as overall accuracy might not be enough to evaluate classifiers and alternative measurements are essentially required. This paper addresses a new model selection problem to construct a tree-based hierarchical classification model based on ensemble of six different classifiers. In our proposed approach, the model selection is tackled as a multi-objective optimization, which not only considers the accuracy of the classification, but also tries to maximize the worst case sensitivity of the multi-class problem. The proposed technique is applied on nine different classes corresponding to various breathing disorders for designing a wearable remote monitoring system. This model correctly classified the respiratory patterns of ten subjects with an accuracy of 99.25% and a sensitivity of 97.78% with detecting the changes in the anterior-posterior diameter of the chest wall during breathing function by means of two accelerometer sensors worn on subject's rib cage and abdomen. The effects of the number of sensors, sensor placement, as well as feature selection on the classification performance are also discussed.
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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