원문정보
초록
영어
This paper deals with a real world problem of medical diagnosis, to this goal, we propose to learn a compact fuzzy medical knowledge base through a cognitively-motivated granular hybrid neuro-fuzzy or fuzzy-neuro possibilistic model appropriately crafted as a means to automatically extract fuzzy weighted production rules. The main idea is to start learning from coarse fuzzy partitions of the involved proteins variations of input variables and proceed progressively toward fine-grained partitions until finding the appropriate partitions that fit the data. We provide details of implementation issues, experimental results, and discussion of interpretability issues. Moreover, learning is firmly grounded on fuzzy relational calculus, linguistic approximation and the crucial notion of importance widely used in human decision making and clinical problem-solving.
목차
1. Introduction and Related Work
2. A Novel Learning Methodology
2.1. Motivations for Our Learning Methodology
3. The Statement of the Learning Problem
3.1. Modeling of the Medical Diagnosis Problem
3.2. Description of the Learning Process
4. Formulation of the Learning Problem
4.1. Hypothesis Generation, Formulation and Testing
4.2. Learning by Hybrid Min-Max Fuzzy-Neuro Network
5. Resolution of the Learning Problem
5.1. The Learning Algorithm and Implementation Issues
5.2. Abstract Computational Model of a Learning Session
6. Experimental Results, Discussion and Interpretability Issues
7. Concluding Remarks and Future Work
References