Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization
Bibliographic record
Abstract
We have designed and implemented an unsupervised learning algorithm for finite mixture model using the scaled Dirichlet distribution for multivariate proportional data. In this paper, the task of learning finite mixture model involves estimation of model parameters as well as inferring the hidden class information of our observed data. We made use of the expectation maximization algorithm to find the maximum likelihood estimate of our model parameters. This work, aims to address the flexibility challenge of the Dirichlet distribution by introducing a distribution that adds to it a scale parameter. This is important, because there is growing need for models that can fully describe the intrinsic nature of datasets. In addition, we applied our learning algorithm to synthetic datasets as well as to address the challenge of detecting fault prone software modules. Our proposed algorithm, makes it possible to discover these fault prone modules by harnessing their complexity-based attribute information. Finally, we compare our proposed model classification results with those from the Gaussian and Dirichlet mixture models.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".