Conference
Processing measure uncertainty into fuzzy classifier
| العنوان: | Processing measure uncertainty into fuzzy classifier |
|---|---|
| المؤلفون: | Monrousseau, Thomas, Travé-Massuyès, Louise, Le Lann, Marie-Véronique, V |
| المساهمون: | Équipe DIagnostic, Supervision et COnduite (LAAS-DISCO), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT) |
| المصدر: | 26th International Workshop on Principles of Diagnosis https://hal.science/hal-01274212 26th International Workshop on Principles of Diagnosis, Aug 2015, Paris, France |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2015 |
| المجموعة: | Université Toulouse III - Paul Sabatier: HAL-UPS |
| مصطلحات موضوعية: | ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications/I.5.4.1: Signal processing, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.3: Deduction and Theorem Proving/I.2.3.8: Uncertainty, ``fuzzy,' and probabilistic reasoning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] |
| جغرافية الموضوع: | Paris, France |
| الوصف: | International audience ; Machine learning such as data based classification is a diagnosis solution useful to monitor complex systems when designing a model is a long and expensive process. When used for process monitoring the processed data are available thanks to sensors. But in many situations it is hard to get an exact measure from these sensors. Indeed measure is done with a lot of noise that can be caused by the environment, a bad use of the sensor or even the conversion from analogic to numerical measure. In this paper we propose a framework based on a fuzzy logic classifier to model the uncertainty on the data by the use of crisp (non fuzzy) or fuzzy intervals. Our objective is to increase the number of good classification results in the presence of noisy data. The classifier is named LAMDA (Learning Algorithm for Multivariate Data Analysis) and can perform machine learning and clustering on different kind of data like numerical values , symbols or interval values. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| Relation: | hal-01274212; https://hal.science/hal-01274212; https://hal.science/hal-01274212/document; https://hal.science/hal-01274212/file/DX15%20-%20Monrousseau,Trave-Massuyes,Le%20Lann%20%28camera-copy%29.pdf |
| الاتاحة: | https://hal.science/hal-01274212 https://hal.science/hal-01274212/document https://hal.science/hal-01274212/file/DX15%20-%20Monrousseau,Trave-Massuyes,Le%20Lann%20%28camera-copy%29.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.3891C5D8 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |