Occupancy Estimation in Smart Buildings: Impact of Data Quality on Feature Selection
Bibliographic record
Abstract
Feature selection has been widely applied in machine learning applications to reduce computational time, improve learning accuracy, and better understand the data modeling process. One of the vital challenges is the correct selection of the relevant features from the available ones in the training dataset. A training dataset of poor quality may compromise the features selection step, which will decrease the generalization capability of the resulting model. Defining the essential features based on data from sensors in smart building applications can significantly reduce solution complexity and total cost. In this paper, an indicator named Qscore is proposed to assess the quality of the training data as an essential step before deploying the feature selection methods. Moreover, a high Qscore value would confirm the reliability of the selected features. To validate the proposed novel concept, the occupancy estimation problem in an office contest is investigated. The training data consist of the measurements collected from standard sensors, for instance, motion detection, power consumption, and CO2 concentration, and the label (i.e., the number of occupants). Several features selection methods along with the proposed data quality indicator and cross-validation approach are deployed to assure the best choice of features in the occupancy estimation problem. Extensive simulations and experiments show the merits of our framework.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".