Learning Case-Based Reasoning Solutions by Association Rules Approach

Similar case retrieval ability is a key technology in CBR(Case Based Reasoning) system. In order to improve the case retrieval efficiency in business financial distress warning(FDW) system, a CBR case retrieval model based on gray relation was proposed, applying the gray relational analysis in case based reasoning for business FDW which improved the deficiency of distance measurement. Moreover, taking into account the different importance of case features in predicting financial distress, a weight vector was defined to solve the influence coming from nor-crucial features. Empirical results proves that this method can effectively improve the case retrieval efficiency of the target enterprise in business FDW system.

Case-based reasoning (CBR) is an artificial intelligence (AI) technique for solving problems. The very fact that CBR draws on past experiences to solve a new problem makes it intuitively appealing as humans also have the same problem-solving behavior. Another advantage of CBR over other conventional AI techniques is that it can work on a shallow knowledge base to start with. These make CBR an excellent method to solve real-life problems and useful in the field like medical diagnosis, engineering diagnosis, product selection, weather prediction, aerospace applications, etc. Classification plays a vital role in the retrieval of cases, as a correct classification results in a correctly retrieved case, which eventually results in a correct solution given by the CBR system. Typically, case retrieval is similarity-based and uses a k-nearest neighbor (k-NN) algorithm. Retrieval aims to find among the stored cases the best match for a given new case. Typically, CBR systems use the nearest neighbor algorithm as a similarity metric for retrieving cases. In this paper, the researchers use the machine learning workbench WEKA to combine well-known classifiers multilayer perceptron and fuzzy-rough nearest neighbor and compare the performance of k-NN with them. They have used benchmark medical data sets to carry out the evaluation process. The experimental results show that the combination of multilayer perceptron and fuzzy-rough nearest neighbor outperforms k-NN to a significant extent for classification, thus effectively improving the case retrieval efficiency and performance.

Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.

Traditional basic oxygen furnace (BOF) prediction based on Case-based reasoning (CBR) is usually dependent on experts and experiences which will result in deviation. Aiming at attributes selection in BOF steelmaking and case base maintenance, this paper proposes using Ridge regression and Association rules to improve CBR and predicting the endpoint carbon content of BOF steelmaking. In CBR, case representation is the basic, the selected attributes in case representation play an important role but dependent on expert experiences so that this paper uses lasso regression algorithm to get the attributes. Through the simulation experiment, the results show that the improved CBR can obviously improve the accuracy of endpoint carbon content prediction.

When the number of samples is large, the scale of the Bayesian network (BN) structure search space increases exponentially with the number of nodes, resulting in a sharp increase in the difficulty of learning the BN structure. Aiming at this problem, this paper proposes a fault diagnosis model construction method combining association rules and a BN network. The Euclidean distance under the Symbolic Aggregation Approximation (SAX) algorithm is utilized to compute and average the distance between the standard and faulty samples and filter the candidate nodes by the average value, which in turn reduces the search sample space. The method of combining Association Rules algorithm with traditional BN structure learning results is used to solve the problem of wrong edges in structure learning. Finally, the maximum likelihood estimation method is used for parameter learning to complete the construction of the diagnostic network. The experimental results show that the running time of the Bayesian Network based on the Association Rules (AR-BN) model proposed in this paper is short and that the Hamming distance with the original structure is small, so this model can effectively reduce the search space and solve the problem of wrong edges, and it also has a good performance in fault diagnosis.

According to characteristics of new problems, the process of finding one or more similar cases from the existing cases to get a new solution is called case-based reasoning (CBR). The kernel idea of CBR is similar in cases having similar solutions. CBR can play its best role only by finding cases that are most similar to new problems through some retrieval methods. Currently, commonly used case retrieval algorithms are basically based on mean operator method. Although the difficulty of calculation is low, the accuracy is limited, and if a certain local similarity is low, the overall result can be affected. We introduce the soft likelihood functions into case retrieval, combine it with KNN, and propose a hybrid retrieval strategy, which is a new and softer way to calculate case similarity. The core of our hybrid retrieval strategy is to aggregate the local similarity and feature similarity of cases by soft likelihood functions, so as to obtain the global similarity. And at the same time, take into account the different attitudinal characteristics of the decision-maker, whether optimistic or pessimistic. The accuracy of this strategy is more than 81\(\%\) in simulation experiments on real data sets, which verifies its effectiveness.

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

Similarity is a core concept in case-based reasoning (CBR), because case base building, case retrieval, and even case adaptation all use similarity or similarity-based reasoning. However, there is some confusion using similarity, similarity measures, and similarity metrics in CBR, in particular in domain-dependent CBR systems. This article attempts to resolve this confusion by providing a unified framework for similarity, similarity relations, similarity measures, and similarity metrics, and their relationship. This article also extends some of the well-known results in the theory of relations to similarity metrics. It appears that such extension may be of significance in case base building and case retrieval in CBR, as well as in various applied areas in which similarity plays an important role in system behavior. © 2002 Wiley Periodicals, Inc.

This chapter is the second chapter in Part II of the book. It is also the basis for Chapter 9, as shown in the shaded area of Fig. 7.1. This chapter first examines the relationship between case-based reasoning (CBR) systems and multiagent systems (MASs), and proposes knowledge-based models of multiagent CBR systems from both logical and knowledge-based viewpoints. Then this chapter investigates the case base and case retrieval in a distributed setting and examines the integration of case-based reasoning capabilities in a BDI architecture. This chapter also discusses CBR for agent team cooperation. Finally this chapter proposes an agent architecture using CBR to model an agent negotiation strategy.

The endpoint carbon content of steelmaking is an important criterion for steel quality. Aiming at increasing the accuracy of endpoint carbon content prediction in basic oxygen furnace (BOF) steelmaking, this paper uses case-based reasoning (CBR) method to predict the endpoint carbon content of BOF steelmaking. In CBR, case retrieval makes a significant impact on reasoning result. Therefore, we apply affinity propagation (AP) clustering algorithm and waterfilling algorithm to enhance the case retrieval so as to improve the accuracy and stability of endpoint carbon content prediction. Through the simulation experiment, this paper compares the new model we proposed with the widely used method at present. The results show that the improved CBR can obviously improve the accuracy of endpoint carbon content prediction.

To improve the performance of electronic commerce recommendation system (ECRS), the system implement frame by case-based reasoning (CBR) and artificial immune system (AIS) is proposed. The inner mechanism of AIS is analyzed, in leaning subsystem of ECRS, the AIS implement cycles such as antigen identifying, initial antibody population generating, affinity calculating, clone selecting, population updating and so on are studied. The key techniques of CBR for reasoning subsystem of ECRS such as case representation, case retrieval, case adaptation and case maintenance are introduced. The ECRS is tested by history record information of some Website, the result demonstrates that both the recommendation accuracy and reasoning response velocity are improved, the ECRS frame based on CBR and AIS is a potent one.

WITHDRAWN: Combined approach for mining high quality interesting association rules

Case retrieval is the focal stage in a Case-Based Reasoning (CBR) system. In this paper, a new case retrieval method called CRGCU is proposed in a systematical way, which is based on Genetic Algorithms (GAs) and Group Decision-Making method. First, feature attribute weights of cases are optimized by using GAs. Second, instead of utilizing only one set of feature attribute weights, we calculate similarity based on multiple sets of optimized weights. The result of case retrieval can be obtained at last by adopting Group Cardinal Utility function. The experiments on both static and dynamic data sets illustrate that our method is effective and suitable for CBR system.

A lot of activities inside human body are carried out intelligently without the explicit intervention of human itself, e.g. various actions of nervous systems, blood circulation system etc. Inspired from these natural systems, autonomic computing is an emerging concept which promises to enable such kind of self-management capabilities inside software systems. Case-based reasoning (CBR) is a methodology to solve current problems using the solutions of past problems of the similar nature. In this paper, we propose to use CBR to achieve self-configuration in autonomic systems. We introduce a new similarity measure to find nearest neighbors. We have also suggested the case preparation, case retrieval and case reuse and refinement methods to enable self-configuration in autonomic systems. To support our proposed methodology, we illustrate a case-study of Autonomic Forest Fire Application.

Retrieval of cases is one important step within the case-based reasoning paradigm. We propose an improvement of this stage in the process model for finding most similar cases with an average effort of O[log 2 n], n number of cases. The basic idea of the algorithm is to use the heterogeneity of the search space for a density-based structuring and to employ this precomputed structure, a k- d tree, for efficient case retrieval according to a given similarity measure. Besides illustrating the basic idea, we present empirical results of a comparison of four different k- d tree generating strategies and introduce the notion of dynamic bounds which significantly reduce the retrieval effort. The presented approach is fully implemented and used within two case-based reasoning systems for classification and diagnostic tasks, Patdex and Inreca.