원문정보
초록
영어
The Case Based Reasoning (CBR) is an approach of solving problem which is based on the reuse, by analogy, of past experiences called case. It is based on the retrieval and adaptation of the old solutions to the new problems. This paper presents a Bayesian adaptation-Guided Retrieval phase for a CBR applied to the diagnosis of hepatic pathologies. The main idea consists in a modelling the case base by a Bayesian Network (BN). Its are excellent tools for modelling the uncertainty in terms of their clear graphic representation as well as the conditional probabilities laws defined on a graph. We are interested to retrieval and adaptation phases. The retrieval phase consists of selecting the most similar case of log linear model by the considering Bayesian Network as a log-linear model on the simplification of the probability. The adaptation phase means modifying solutions of retrieved cases to fit the current problem. The dependence between these two phases is defined by two measures: a similarity measure and an adaptation measure. The objective of this dependence is to guarantee the retrieved case which is the easiest to adapt and improve the performance of CBR. An example of the diagnosis of the hepatic pathologies will illustrate the presented approach.
목차
1. Introduction
2. Related works
3. CBR Cycle
4. Presentation of Bayesian network
4.1. Definition of Bayesian Network
4.2. Inference
5. Integrating Bayesian Network in CBR
5.1 Network Variables Definition
5.2 Definition of Weight
5.3 The Case Base Architecture
5.4 Bayesian Case Description
5.5 Log linear Models
6. The Proposed Retrieval Phase
6.1 Process of Initialization
6.2 Process of Propagation (Extension of Pearl Algorithm)
6.3 Process of Retrieval
7. The Proposed Adaptation Phase
7.1 The Adaptation Measure Definition
7.2 Algorithm of the Proposed Adaptation Phase
7.3 Illustration of Adaptation Phase
8. Discussion
9. Conclusion and Perspectives
References