RUMC: A Rule-based Classifier Inspired by Evolutionary Methods

As the field of data analysis grows rapidly due to the large amounts The Rule Aggregating ClassifiER (RACER) [7] is a rule-based of data being generated, effective data classification has become increasingly classification algorithm that generates initial rules from training important. This paper introduces the RUle Mutation Classifier dataset records with the same mechanism. However, these rules (RUMC), which represents a significant improvement over the tend to be too specific, making them less effective for classifying Rule Aggregation ClassifiER (RACER). RUMC uses innovative rule new data, particularly when working with small datasets that have mutation techniques based on evolutionary methods to improve few distinct instances. To address this challenge, I introduce the classification accuracy. In tests with forty datasets from OpenML RUle Mutation Classifier (RUMC), a novel algorithm that enhances and the UCI Machine Learning Repository, RUMC consistently outperformed the capabilities of RACER. RUMC aims to improve the handling of twenty other well-known classifiers, demonstrating its various datasets, including high-dimensional and low-sample-size ability to uncover valuable insights from complex data.