Application of the Statistical Test Rule Induction Method to Classification Problems and Comparisons with the Neural Network Method

After pointing out the problems of the conventional Rough Sets' methods that induce if-then rules hidden in a dataset called the decision table (DT), we proposed a new model for generating the DT and developed a method, statistical test rule induction method (STRIM), for inducing rules in the DT from a statistical perspective. The validity and effectiveness of STRIM were confirmed by applying it to the DT generated based on the data generation model (DGM). In general, the effectiveness of the methods inducing rules is often confirmed by applying them to classification problems that predict outputs against new inputs. From this perspective, we applied an expanded STRIM (ex-STRIM) to a classification problem and confirmed its effectiveness. This paper applies a Feedforward Neural Network and a modified ex-STRIM to the classification problem under the DGM and compares their classification performance and features as a classification method. Although the classification results strongly depend on the latent rules in the dataset and the number of learning datasets, the similarities and differences between the two methods are discussed; for example, both methods use different approaches to realize a conditional probability distribution of the output, given the input.