A hybrid knowledge discovery system for oil spillage risks pattern classification
Bibliographic record
Abstract
The complexity and the dynamism of oil spillages make it difficult for planners and responders to produce robust plans towardstheir management. There is need for an understanding of the nature, sources, impact and responses required to prevent or controltheir occurrence. This paper develops an intelligent hybrid system driven by Sugeno-Type Adaptive Neuro Fuzzy InferenceSystem (ANFIS) for the identification, extraction and classification of oil spillage risk patterns. Dataset consisting of 1008records was used for training, validation and testing of the system. Result of sensitivity analysis shows that Cause, Locationand Type of spilled oil have cumulative significance of 85.1%. Optimal weights of Neural Network (NN) were determined viaGenetic Algorithm with hybrid encoding scheme. The Mean Squared Error (MSE) of NN training is 0.2405. NN training,validation and testing results yielded R > 0.839 in all cases indicating a strong linear relationship between each output andtarget data. Rule pruning was performed with support (15%) and confidence (10%) minimum thresholds and antecedent-size of3. The performance of the ANFIS was evaluated with eight different types of membership functions (MFs) and two learningalgorithms. The model with triangular MF gave the best performance among all other given models while hybrid-learningalgorithm performed better than back propagation algorithm. The ANFIS model reported in the paper adopted triangular MFand hybrid learning algorithm for the predication and classification of oil spillage risk patterns. Average training and testingMSE of the model is 0.414315 and 0.221402 respectively. The knowledge mining results show that ANFIS based systemsprovide satisfactory results in the prediction and classification of oil spillage risk patterns.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".