Benchmarking a coevolutionary streaming classifier under the individual household electric power consumption dataset
Bibliographic record
Abstract
The application of genetic programming (GP) to streaming data analysis appears, on the face of it, to be a less than obvious choice. If nothing else, the (perceived) computational cost of model building under GP would preclude its application to tasks with non-stationary properties. Conversely, there is a rich history of applying GP to various tasks associated with trading agent design for currency and stock markets. In this work, we investigate the utility of a coevolutionary framework originally proposed for trading agent design to the related streaming data task of predicting individual household electric power consumption. In addition, we address several benchmarking issues, such as effective preprocessing of stream data using a candlestick representation originally developed for financial market analysis, and quantification of performance using a novel `area under the curve' style metric for streaming data. The computational cost of evolving GP solutions is demonstrated to be suitable for real-time operation under this task and shown to provide classification performance competitive with current established methods for streaming data classification. Finally, we note that the individual household electric power consumption dataset is more flexible than the more widely used electricity utility prediction dataset, because it supports benchmarking at multiple temporal time scales.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
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 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".