Bridging the gap between data mining and decision support: A case-based reasoning and ontology approach
Bibliographic record
Abstract
Nowadays, decision makers invariably need to use decision support technology (DS) such as data mining (DM) methodologies and tools in order to tackle complex decision making problems. However the successful application of DM technology requires that one possess specific DM decision-making skills. F or instance, the effective application of a data mining process is littered with many difficult and technical decisions (i.e. data cleansing, feature transformations, algorithms, parameters, evaluation, etc.) In essence, this contentious problem and burden for decision makers clearly stems from a poor DM-DS integration. As a result, we have strived to improve on this problem by proposing an intelligent DM assistant that can potentially empower decision makers to better leverage DM technology and achieve their intended business objectives. Nonetheless, as this paper will strive to demonstrate, the realization of an intelligent data mining assistant for the decision maker or non-specialist data miner is a challenging and complex endeavour. Hence, in what follows we present the key design considerations (i.e. knowledge representation and reasoning, knowledge elicitation and reuse efforts, etc.) that were addressed during the implementation of a hybrid data mining assistant, based on the case-based reasoning (CBR) paradigm and the use of a formal OWL-DL ontology.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".