Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Strategies for efficient incremental nearest neighbor search
Pattern Recognition
Readings in uncertain reasoning
C4.5: programs for machine learning
C4.5: programs for machine learning
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
User profiling with Case-Based Reasoning and Bayesian Networks
International Joint Conference, 7th Ibero-American Conference, 15th Brazilian Symposium on AI, IBERAMIA-SBIA 2000, Open Discussion Track Proceedings on AI
Retrieving Adaptable Cases: The Role of Adaptation Knowledge in Case Retrieval
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
An Accurate Adaptation-Guided Similarity Metric for Case-Based Planning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Question classification with log-linear models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Rapid retrieval algorithms for case-based reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
PCOPM: a probabilistic CBR framework for obesity prescription management
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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Case-based reasoning CBR is an approach of solving problem, which is based on the reuse, by analogy, of past experiences called 'case'. Retrieval of cases is a primary step in CBR, and the similarity measure plays a very important role in case retrieval. We present a probabilistic retrieval phase applied to the diagnosis of hepatic pathologies. The main idea consists in modelling the case base by a Bayesian network BN. BN is an excellent tool for modelling the uncertainty in terms of their clear graphic representation as well as the conditional probabilities laws defined on. In this paper, a retrieval phase is attended, which consists of selecting the most similar case of log-linear model by considering Bayesian network as a log-linear model on the simplification of the probability. Also, we used two exact algorithms of inference: Pearl and JLO-Jensen, Lauritzen and Olesen - to calculate the conditional probabilities and to optimise the retrieval phase. The objective of this work is to improve the performance of CBR. Experimental results show that the proposed methods improve the efficiency of case retrieval.