Towards Inductive Learning of Complex Fuzzy Inference Systems
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
Complex fuzzy logic is an extension to type-1 fuzzy sets that has recently been developed. To date, no practical applications of complex fuzzy logic have been developed, possibly due to the difficulty of eliciting expert knowledge for both the magnitude and phase of a complex fuzzy set. We believe that practical applications of complex fuzzy logic require inductive learning. We are taking a first step towards this by building an inductive learning algorithm ANCFIS (Adaptive Neuro Fuzzy Complex Inference System), which hybridizes the theory of complex fuzzy inference and ANFIS. We believe that complex fuzzy sets will be a remarkably efficient way of modeling approximately periodic data. Thus, our proposed application of ANCFIS is in time series forecasting. We present an introduction to ANCFIS, its structure and computational formulas. The ANCFIS architecture is tested against three commonly cited time series datasets. Preliminary results show that ANCFIS is indeed able to model relatively periodic data as expected.
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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 | 0.001 | 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 | 0.000 |
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 it