Ensemble Methods to Optimize Automated Text Classification in Avatar Therapy
Bibliographic record
Abstract
Background: Psychotherapeutic approaches such as Avatar Therapy (AT) are novel therapeutic attempts to help patients diagnosed with treatment-resistant schizophrenia. Qualitative analyses of immersive sessions of AT have been undertaken to enhance and refine the existing interventions taking place in this therapy. To account for the time-consuming and costly nature and potential misclassification biases, prior implementation of a Linear Support Vector Classifier provided helpful insight. Single model implementation for text classification is often limited, especially for datasets containing imbalanced data. The main objective of this study is to evaluate the change in accuracy of automated text classification machine learning algorithms when using an ensemble approach for immersive session verbatims of AT. Methods: An ensemble model, comprising five machine learning algorithms, was implemented to conduct text classification for avatar and patient interactions. The models included in this study are: Multinomial Naïve Bayes, Linear Support Vector Classifier, Multi-layer perceptron classifier, XGBClassifier and the K-Nearest-Neighbor model. Accuracy, precision, recall and f1-score were compared for the individual classifiers and the ensemble model. Results: The ensemble model performed better than its individual counterparts for accuracy. Conclusion: Using an ensemble methodological approach, this methodology might be employed in future research to provide insight into the interactions being categorized and the therapeutical outcome of patients based on their experience with AT with optimal precision.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".