Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System

Abstract

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest. This is because there are multiple stages in the IFS by considering non-membership functions.