ESIREOS: Efficient, Scalable, Internal, Relative Evaluation of Outliers Solutions
Bibliographic record
Abstract
Anomaly (outlier) detection is one of the main tasks of data mining. Since anomalies can translate into important information in numerous fields, several methods have been developed to identify them. Unsupervised methods for outlier detection, which is the focus of this work, have become increasingly important due to the lack of labeled data in many applications. A common challenge when dealing with unsupervised methods, however, is how to evaluate the quality of their results. Without labels available, one has to rely on the so-called internal evaluation, which is based solely on the data and the assessed solutions. In this context, IREOS was proposed as the first internal evaluation measure for unsupervised anomaly detection. IREOS allows one to select better solutions (algorithms, parameters) for a given problem using only intrinsic information from the data. One major limitation of IREOS, however, is the demand to train many highly complex classifiers, which makes it impractical for large datasets. In this work, we propose the first Efficient, Scalable version of IREOS, ESIREOS. We address the computational performance shortcomings of IREOS by using Massive Parallel Computing (MPC) techniques that efficiently implement horizontal computational scaling for many machine learning problems. ESIREOS also makes use of approximated nearest neighbor graphs (NNGs) to reduce the volume of data and processing power demanded by IREOS without any significant loss in the quality of the results. We evaluate ESIREOS theoretically by estimating its asymptotic complexity and empirically with experiments on real and synthetic datasets to assess its effectiveness and efficiency compared to the original version. Our results showed that ESIREOS significantly improved the computational runtime compared to the original IREOS while maintaining quality. Also, ESIREOS proved capable of evaluating solutions for very large datasets, even those which IREOS cannot evaluate in a feasible time. Therefore, this efficient and scalable new version can be used in many scenarios, mainly, but not limited to, those with large or distributed data.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".