Fuzziness for classification and visual query interface: platform independent query model with self-adaptive fuzzy capabilities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».