Earticle

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

Abstract
 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

키워드

저자

• Mokhtar Beldjehem [ University of Ottawa, School of Information Technology and Engineering ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605