基于进化方法的规则突变分类器，提升复杂数据分类准确率。
RUMC: A Rule-based Classifier Inspired by Evolutionary Methods
- 用进化思想设计规则突变机制，动态优化分类规则。
- 在40个公开数据集上超越20种主流分类器，表现稳定领先。
- 适合需要高可解释性与强泛化能力的数据分类任务。
随着数据量激增，有效数据分类愈发重要。本文提出基于进化方法的规则突变分类器（RUMC），相比规则聚合分类器（RACER）有显著改进。RUMC采用创新的规则突变技术，提升分类准确性。在OpenML和UCI机器学习仓库的40个数据集上，RUMC持续优于20种知名分类器，展现出从复杂数据中挖掘有价值洞察的能力。
原文摘要 · Abstract (English)
As the field of data analysis grows rapidly due to the large amounts of data being generated, effective data classification has become increasingly important. This paper introduces the RUle Mutation Classifier (RUMC), which represents a significant improvement over the Rule Aggregation ClassifiER (RACER). RUMC uses innovative rule mutation techniques based on evolutionary methods to improve classification accuracy. In tests with forty datasets from OpenML and the UCI Machine Learning Repository, RUMC consistently outperformed twenty other well-known classifiers, demonstrating its ability to uncover valuable insights from complex data.
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