RUMC: A Rule-based Classifier Inspired by Evolutionary Methods

Autor:	Mokhtari, Melvin
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 3 tables, 2 pseudocodes, 1 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.07885 Zobrazit plný text záznamu View this record from Arxiv