Bibliographic record
Abstract
Self-optimal clustering, in comparison with other clustering methods, includes features that, when optimizing in such environments, these characteristics should be considered.A variety of methods for clustering have already been proposed that each of these methods looks at the environment with specific approach and have optimized clustering methods by inspiration of different algorithms.In this research, we have used the Fuzzy Q learning algorithm for the first time.In a Fuzzy Q learning problem, we face with an autonomous agent that interacts with environment through trial and error, and learns to select the optimal action to reach the goal.In the Fuzzy Q learning model, the agent moves into the environment and remembers the related states and rewards.The agent tries to behave in such a way that maximizes the reward function.Since Fuzzy Q learning algorithm uses the combination of reinforcement learning and fuzzy logic, it is an appropriate option for solving this group of problems.We first define the Q learning algorithm in this thesis.And after proposing this algorithm, we will shortly investigate how to improve it by fuzzy logic, which leads to the suggestion of a fuzzy reward function to reduce the complexity of clustering, and express the efficiency of proposed algorithms by standard and appropriate tests.The results of tests indicate that the proposed method has acceptable efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.905 | 0.925 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".