Function to Flatten Gesture Data for Specific Feature Selection Methods to Improve Classification
Bibliographic record
Abstract
Gestures are pieces of information with characteristics such as: multiple and chronologically linked samples with different length. The gesture characteristics mentioned before make classification, of this type of data, a challenging task. We studied the effects of flattening gesture data. We proposed a function to represent gestures in a flat format taking in consideration the evolution sense they possess. The function's main goal is to compare gestures intra class to spot differences. This function is described step by step and then its outcome is used as input to two feature selection methods (Bayesian network / Markov blanket and Logical Combinatorial to Pattern Recognition). After, with the subsets obtained, we trained Hidden Markov Models machines. We found that applying our methodology to gesture data, the subset of attributes obtained (feature selection) were able to classify with accuracies of 0.88 and 0.87 of a maximum of 0.90. The maximum accuracy was obtained from an exhaustive classification exercise we performed in order to compare our results. These findings suggest that our methodology can be applied over raw data (gesture data or any chronologically linked data) without the need of experts to transform data (i.e. feature extraction).
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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 | 0.001 | 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".