Increasing the explainability and success in classification: many-objective classification rule mining based on chaos integrated SPEA2.

ABSTRACT: Classification rule mining represents a significant field of machine learning, facilitating informed decision-making through the extraction of meaningful rules from complex data. Many classification methods cannot simultaneously optimize both explainability and different performance metrics at the same time. Metaheuristic optimization-based solutions, inspired by natural phenomena, offer a potential paradigm shift in this field, enabling the development of interpretable and scalable classifiers. In contrast to classical methods, such rule extraction-based solutions are capable of classification by taking multiple purposes into consideration simultaneously. To the best of our knowledge, although there are limited studies on metaheuristic based classification, there is not any method that op

SUBMITTER: Yildirim S 

PROVIDER: S-EPMC11419619 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature