ANCFIS-ELM: A machine learning algorithm based on complex fuzzy sets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Adaptive Neuro-Complex Fuzzy Inferential System was the first neuro-fuzzy system employing complex fuzzy sets and rule interference. It was shown to be both accurate and parsimonious in time series forecasting. The main disadvantage of this system is its slow learning algorithm. One possible approach to speeding up this neuro-fuzzy system is to apply concepts from the Extreme Learning Machine family of architectures; specifically, we will randomly select the parameters of a “pool” of complex fuzzy sets, and then train the neural network by incrementally updating the parameters of a linear output function. We evaluate this new architecture on four software reliability growth datasets (a particular instance of time series forecasting).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it