Bibliographic record
Abstract
Ahstract-This paper presents our work on a novel intrusion detection classifier based on asymmetric Gaussian mixture (AGM) model and reversible jump Markov chain Monte Carlo (RJMCMC) learning algorithm. Previous efforts reveal the fact that AGM outperforms classic Gaussian mixture model (GMM) by taking asymmetric datasets into consideration which provides more flexibility. Our RJMCMC implementation is based on a hybrid sampling-based approach which takes advantages of both Metropolis-Hastings (MH) and Gibbs sampling methods, therefore, simplifies mathematical complexity and extends adaptability of the model. Moreover, without giving a fixed components number in advance, RJMCMC applies a dynamic data-based strategy to identify the optimal components number throughout iterations which makes the model learning a self-adaptive process. Since the model is nondeterministic, Laplace approximation based marginal likelihood will be calculated for multiple runs as model selection procedure to improve the correctness and fitting accuracy. Both synthetic and challenging intrusion detection datasets are applied to our model to discover its merits.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".