Author : Mohamed Benamina
Affiliation : Laboratoire d’Informatique d’Oran (LIO) University of Oran 1 Ahmed Ben Bella BP 1524, El M'Naouer Es Senia
Country : Algeria
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 8, 18, February, 2018
The classification by inductive learning finds its originality in the fact that humans often use it to resolve and to handle very complex situations in their daily lives. However, the induction in humans is often approximate rather than exact. Indeed, the human brain is able to handle imprecise, vague, uncertain and incomplete information. Also, the human brain is able to learn and to operate in a context where uncertainty management is indispensable. In this paper, we propose a Boolean model of fuzzy reasoning for indexing the monitoring sub-plans, based on characteristics of the classification by inductive learning. Several competing motivations have led us to define a Boolean model for CBR knowledge base systems. Indeed, we have not only desired experiment with a new approach to indexing of cases by fuzzy decision tree, but we also wanted to improve modelling of the vague and uncertain of the natural language concepts, optimize response time and the storage complexity.
Keyword : Boolean Modelling, Cellular Machine, Case-Based Reasoning, Diabetes Diagnosis, Fuzzy Reasoning, Planning.
For More Details : https://airccj.org/CSCP/vol8/csit89803.pdf
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