Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Hybrid System <s> A major bottleneck in developing knowledge-based systems is the acquisition of knowledge. Machine learning is an area concerned with the automation of this process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge-based systems use higher-level knowledge representations. the method we propose here provides a technique that automatically allows us to extract conjunctive rules from the lower-level representation used by neural networks, the strength of neural networks in dealing with noise has enabled us to produce correct rules in a noisy domain. Thus we propose a method that uses neural networks as the basis for the automation of knowledge acquisition and can be applied to noisy, realworld domains. © 1993 John Wiley & Sons, Inc. <s> BIB001 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Hybrid System <s> This paper describes a system that responds to a significant problem involving credit card theft. After a card has been stolen, some of the transactions processed subsequently have to be granted if it seems that they were carried out by the card owner himself (e.g. by a standing order) while other transactions should be denied authorization if they were carried out by the misuser of the card. The system developed makes use of a neural network integrated with a Case-Based Reasoning (CBR) component. While the CBR system can use specific cases to determine when a transaction should be granted or denied authorization, the neural network can recognise general patterns of behaviour for the use and the misuse of credit cards and can use this knowledge in the classification task. This paper presents the architecture of the classification system proposed, highlighting the main aspects of each of its components. The system's performance is evaluated and conclusions are drawn. <s> BIB002 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Hybrid System <s> This paper presents a new approach for learning general knowledge in a diagnostic case-based system through the use of a neural network. We take advantage of the self-adapting nature of the neural network to discover the most relevant features and combination of features for each diagnosis considered. The knowledge acquired by the network is interpreted and mapped into symbolic diagnosis descriptors, which are kept and used by the case-based system to guide its reasoning process, to retrieve cases from a case library and to build explanations. The neural network used in the learning process was the Combinatorial Neural Model, a network that has been combined with other symbolic approaches previously. The paper presents the method used to interpret the knowledge learned in the neural network, as well as the guidelines followed by the reasoning process of the CBR system. An initial experiment in clinical psychology is also reported, where the case-based model introduced here was used to learn and represent the psychological profile of patients in evaluation for heart transplant. <s> BIB003 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Hybrid System <s> The extraction of knowledge from databases may be accomplished through the application of various machine learning and artificial intelligence methods. A hybrid approach is presented which uses a combination of case-based reasoning and artificial neural networks. The approach is illustrated through its application, in different ways, to two diverse practical domains: (i) the design of masonry panels, and (ii) the real-time forecasting of physical parameter values of the ocean. The results obtained to date are presented and the further continuation of the approach in the area of civil engineering design is discussed. <s> BIB004
In CBR systems, general domain knowledge and particular knowledge represented by instances play an equally important role in problem solving. A hybrid architecture integrating a CBR system with a neural network component has been developed to use general and specific knowledge in the reasoning process BIB002 BIB003 . In such a hybrid system, the neural network itself is not involved in case matching, case retrieval and the reasoning process. Instead, the network is trained to learn general domain knowledge and, as a result, the trained network can be used as a source of general knowledge. Furthermore, knowledge stored in the network is interpreted as a symbolic representation, which is combined with specific cases to support the reasoning. The Combinational Neural Model (CNM)   was employed in the system. As shown in Fig. 5 , the CNM has a feedforward topology with three layers. Each node represents a symbolic concept. The activation state of a node is described by a value in the interval [0,1], which can be interpreted as the degree of confidence that the network places in the concept represented by the specific node. Nodes in each two adjacent layers are connected by a set of weights, which can be understood as memberships possessed by the concepts described by the nodes in the lower layer to that described in the higher layer. When an input, represented by a set of fuzzy numbers, is presented, a logical operation is performed, layer-by-layer, in the network. Each input node takes either an excitatory or inhibitory state. An excitatory node sends the arriving signal x, multiplied by its weight. An inhibitory node preforms the fuzzy negation on the arriving signal, transforming it in 1-x, before propagating it, using its weight as an attenuating factor. The combinational layer is formed by hidden fuzzy ANDnodes, which perform different combinations of the input signals. The output layer contains a set of OR-nodes. These nodes implement a competitive mechanism among the all different arriving signals. The main advantage of using the CNM for learning general domain knowledge is that, because of the specific choices of transfer functions of nodes, the connecting weights have a clear meaning, and can easily be explained and justified. Therefore, knowledge embodied in the connecting weights can be further extracted and interpreted as a symbolic representation. In contrast to Reategui and Campbell BIB002 and Reategui et al. BIB003 , a hybrid system proposed in Lees et al. BIB004 uses a RBF network in the aid of the CBR adaptation process. Some similar hybrid systems can be found, (see, for example, Yao and He  ). In addition, some other work concerning eliciting symbolic knowledge representation from a trained neural network has been reported  BIB001  . By using these methods, the neural network can be incorporated with a CBR system more effectively for problem-solving.