Increasing Prediction Accuracy for Human Activity Recognition Using Optimized Hyperparameters
Bibliographic record
Abstract
In order to provide context-aware services such as health monitoring and customized energy consumption, smart environment designers need to design robust systems for recognizing the Activities of Daily living (ADL). Once these activities are recognized, the data collected can be used for prediction. For example, energy consumption and other characteristics in the home can be predicted. This is possible if human activity in a smart home can be forecasted. The aim of this research work is to "find the best machine learning algorithm to predict human activities and to use hyperparameters tuning through performance optimization to improve the accuracy of the algorithm" The results of our initial experiments using default hyper-parameters show that Random Forest, compared to four other algorithms (MLP, SVM, Naïve Bayes, and Decision Tree), has the best accuracy at 65.32% for all features and 62.54% for reduced number of features. Tuning four Random Forest hyperparameters through optimization increases the accuracy to 97.9777% for all features and 98.287% for reduced features respectively. Using the optimized Random Forest hyperparameters, 20,000 data points are forecasted with MAE of 0.0098 compared to 0.0445 for Support Vector Machine (SVM).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
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".