Bayesian Classifier with K-Nearest Neighbor Density Estimation for Slope Collapse Prediction
Bibliographic record
Abstract
Bayesian Classifier with K-Nearest Neighbor Density Estimation for Slope Collapse Prediction Min-Yuan Cheng, Nhat-Duc Hoang, Nai-Wen Chang Pages 267-274 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Heavy rainfall and typhoon oftentimes cause the collapse of hillslopes across mountain roads. Disastrous consequences of slope collapses necessitate the approach for predicting their occurrences. In practice, slope collapse prediction can be formulated as a deterministic classification problem with two class labels, namely collapse and non-collapse. Nevertheless, due to the criticality and the uncertainty of the problem, evaluating the collapse susceptibility of an area is a challenging task. This study proposes a novel Artificial Intelligence (AI) approach, named as K-Nearest Neighbor Based Bayesian Classifier (KNNBC), to deal with slope collapse assessment. In the proposed model, Bayesian inference is used as a framework to achieve probabilistic prediction of slope collapse. Meanwhile, K-Nearest Neighbor (K-NN) is employed as a density estimation technique. Equipped with probabilistic outputs, the K-NNBC is able to yield predictions with different levels of confidence and diminish misclassified cases. Experimental results point out that the proposed model is very helpful for decision-makers in slope collapse assessment and disaster prevention planning. Keywords: Slope Collapse Prediction; Bayesian Inference; K-Nearest Neighbor; Probabilistic Classification DOI: https://doi.org/10.22260/ISARC2013/0029 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".