CLASS: a general approach to classifying categorical sequences
Bibliographic record
Abstract
The rapid burgeoning of available data in the form of categorical sequences, such as biological sequences, natural language texts, network and retail transactions, makes the classification of categorical sequences increasingly important. The main challenge is to identify significant features hidden behind the chronological and structural dependencies characterizing their intrinsic properties. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but categorical sequences often have similar features in non-chronological order. In addition, these algorithms have serious difficulties in outperforming domain-specific algorithms. In this paper we propose CLASS, a general approach for the classification of categorical sequences. By using an effective matching scheme called SPM for Signifi cant Patterns Matching, CLASS is able to capture the intrinsic properties of categorical sequences. Furthermore, the use of Latent Semantic Analysis allows capturing semantic relations using global information extracted from large number of sequences, rather than comparing merely pairs of sequences. Moreover, CLASS employs a classifier called SNN for Significant Nearest Neighbours, inspired from the K Nearest Neighbours approach with a dynamic estimation of K, which allows the reduction of both false positives and false negatives in the classification. The extensive tests performed on a range of datasets from different fields show that CLASS is oftentimes competitive with domain-specific approaches.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".