FAST-ODT: A Lightweight Outlier Detection Scheme for Categorical Data Sets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Outlier detection is a key data analysis technique that aims to find unusual data objects in a data set. It has been widely used in varied areas, including communication networks, finance, medicine, environmental studies, etc. Many applications in these areas involve categorical data. For example, the data set used in the application of intrusion detection normally includes a group of captured packets, which tend to have categorical attributes such as “protocol”. Although there are many outlier detection algorithms for applications involving numerical data, only a few existing schemes can handle categorical data. And the schemes designed for categorical data seriously suffer from two problems: low detection precision and high time complexity. In this paper, we present two novel outlier detection algorithms for categorical data sets. First of all, we describe a simple scheme based on entropy, Outlier Detection Tree (ODT). With ODT, a classification tree is constructed to classify the data set into two classes: a normal class and an abnormal class. Thereafter, each data object is identified as an outlier or a normal one using the if-then rules in the tree. Furthermore, we propose an advanced outlier detection algorithm, FAST-ODT, which achieves both high detection accuracy and low time complexity. Our experimental results indicate that FAST-ODT outperforms the existing algorithms in terms of outlier detection precision and computational complexity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it