Bibliographic record
Abstract
This thesis presents an agent-based cooperative data mining model named CoLe2. CoLe 2 is targeted at performing data mining on large, heterogeneous data sets. It employs multiple different types of data mining algorithms, enables cooperations among these algorithms, and produces combined results in the form of rules. CoLe2 is a multi-agent system with three types of agents that have the different roles of running data mining algorithms, performing combination of mining results, and driving the entire CoLe2 system work flow with knowledge-based strategies, respectively. The system has a work flow with two levels of loops. The outer loop performs data selection, mining algorithm selection and expectation adjustment strategies. The inner loop performs data mining execution and result combination, with additional knowledge-based strategies implemented in the agents. The agents exchange useful information during the running of the work flow to help each other. A prototype system of the CoLe2 model is described. This prototype contains four different data mining algorithms (a classification algorithm, a sequence mining algorithm, an association rules mining algorithm and a descriptive mining algorithm), two combination strategies and instantiations of the knowledge-based strategies. The strategies instantiations include data selection based on a clustering algorithm, an asynchronous work flow for better turnaround time, relevance factor calculation, fuzzy condition matching, prediction histogram based rule similarity and rule grouping. Experiments have been performed with two data sets – a medium-sized data set of billing data from Calgary Health Region, and a large data set from the Alberta Kidney Disease Network. The experimental results show advantages of CoLe2 over individual data mining algorithms in terms of efficiency and result quality, as well as advantages over the CoLe model with only one level of work flow. Specialized experiments also prove the effectiveness of individual knowledge-based strategies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".