A NOVEL DISTANCE FUNCTION: FREQUENCY DIFFERENCE METRIC
Bibliographic record
Abstract
A high quality distance function that measures the difference between instances is essential in many real-world applications and research fields. For example, in instance-based learning, the distance function plays the most important role. A large number of distance functions have been proposed. For nominal attributes, Value Difference Metric (VDM) is one of the state-of-the-art and widely used distance functions. However, it needs to estimate the conditional probabilities, which drops its efficiency in computing the distance between instances. Besides, a practical issue that arises in estimating the conditional probabilities is that the denominators can be zero or very small. This makes them either undefined or very large. Therefore, an efficient distance function that can measure the difference between two instances but without the practical issue confronting VDM is desirable. In this paper, we propose a novel distance function: Frequency Difference Metric (FDM). FDM is just based on the joint frequencies of class labels and attribute values, instead of the conditional probabilities. Extensive empirical studies show that FDM performs almost as well as VDM in terms of accuracy, but significantly outperforms VDM in terms of efficiency. This work provides a very simple, efficient, and effective distance function that can be widely used in many real-world applications and research fields.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".