Variations on Associative Classifiers and Classification Results Analyses
Bibliographic record
Abstract
The chapter introduces the associative classifier, a classification model based on association rules, and describes the three phases of the model building process: rule generation, pruning, and selection. In the first part of the chapter, these phases are described in detail, and several variations on the associative classifier model are presented within the context of the relevant phase. These variations are: mining data sets with re-occurring items, using negative association rules, and pruning rules using graph-based techniques. Each of these departs from the standard model in a crucial way, and thus expands the classification potential. The second part of the chapter describes a system, ARC-UI that allows a user to analyze the results of classifying an item using an associative classifier. This system uses an intuitive, Web-based interface and, with this system, the user is able to see the rules that were used to classify an item, modify either the item being classified or the rule set that was used, view the relationship between attributes, rules and classes in the rule set, and analyze the training data set with respect to the item being classified.Request access from your librarian to read this chapter's full text.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".