Neuro-Fuzzy-Rough Classification for Improving Efficiency and Performance in Case-Based Reasoning Retrieval

Abstract

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.