Fuzziness for classification and visual query interface: platform independent query model with self-adaptive fuzzy capabilities
Bibliographic record
Abstract
Imprecision provides a range of freedom in expressing personal and group opinions. It is the common style for expressing opinions in formal and informal daily communications. However, the research community neglected imprecision until 1960s and all automated database management systems are still based on crisp values. Thanks to Prof. Zadeh who initiated the research efforts on fuzziness; his seminal work in 1960s stimulated, prepared for and produced the current interest to incorporate imprecision (fuzziness) in automated systems. In general, fuzziness can be specified using one of three alternative approaches, namely manually by the user, semi-automated or fully automated. These three approaches are considered and investigated further in my research described in this dissertation. I demonstrate the effectiveness and applicability of these alternatives by concentrating on data mining and query coding. In particular, I am describing how they can be incorporated in building classification model and user-centric query interface. A classification model enriched with fuzziness expresses the classification rules using fuzzy terms which are more understandable by humans whether experts or naive. In other words, the outcome from the classifier model will be expressed using fuzzy terms instead of crisp values. Though the three alternative approaches can be equally applied in building the classifier model, this study concentrates on employing the semi-automated method to specify the fuzzy sets and their corresponding membership functions. The user-centric query interface is very common application that allows expressing both the input and the output using fuzzy terms. This is becoming a need in the evolving internet-based era where web-based applications are very common and the number of users accessing structured databases is increasing rapidly. Restricting the user group to only experts in query coding must be avoided. In this regard, I developed a user-friendly visual user interface that facilitates expressing queries using both fuzziness and traditional method. The fuzziness is not expressed explicitly inside the database; fuzziness is absorbed and effectively handled by an intermediate layer which is incorporated between the front-end visual user-interface and the back-end database. Test results and user studies demonstrate the applicability and effectiveness of the proposed approach to handle and deal with fuzziness. My results should open the gate wide for other applications to consider fuzziness as described in this thesis. Index Terms: classification, fuzziness, user interface, query coding, visualization, user study.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".