Bibliographic record
Abstract
Research in intelligent information systems investigates the possibilities of enhancing their over-all performance, particularly their prediction accuracy and time complexity. One such discipline, data mining (DM), processes usually very large databases in a profound and robust way (Fayyad et al., 1996). DM points to the overall process of determining a useful knowledge from databases, that is, extracting high-level knowledge from low-level data in the context of large databases. This article discusses two newer directions in this field, namely knowledge combination and meta-learning (Vilalta & Drissi, 2002). There exist approaches to combine various paradigms into one robust (hybrid, multistrategy) system which utilizes the advantages of each subsystem and tries to eliminate their drawbacks. There is a general belief that integrating results obtained from multiple lower-level decision-making systems, each usually (but not required) based on a different paradigm, produce better performance. Such multi-level knowledgebased systems are usually referred to as knowledge integration systems. One subset of these systems is called knowledge combination (Fan et al., 1996). We focus on a common topology of the knowledge combination strategy with base learners and base classifiers (Bruha, 2004). Meta-learning investigates how learning systems may improve their performance through experience in order to become flexible. Its goal is to search dynamically for the best learning strategy. We define the fundamental characteristics of the meta-learning such as bias, and hypothesis space. Section 2 surveys the various directions in algorithms and topologies utilized in knowledge combination and meta-learning. Section 3 represents the main focus of this article: description of knowledge combination techniques, meta-learning, and a particular application including the corresponding flow charts. The last section presents the future trends in these topics.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".