A hybrid CBR and BN architecture refined through data analysis

Abstract

The overall goal of this research is to study reasoning under uncertainty by combining Bayesian Networks and Case-Based Reasoning through constructing an experimental decision support system for classification of cancer pain. We have experimentally analysed a medical dataset in order to reveal properties of the data with respect to properties of the two reasoning methods. We also preprocessed our medical data with help from a clinical expert, which resulted in four data sets with different characteristics. This culminates in a hybrid system architecture, where CBR handles the exceptions or outliers with respect to the distribution of the data and the target class, while BN handles the more common situations. Through a set of experiments under varying conditions we show that a hybrid BN+CBR system is favorable over each single method.