Using the ECOC Algorithm to Classify Diabetes with Fuzzy-Mapped Decoding Method
Ying Bai, Dali Wang
Source record
Source: Crossref
Published: Jan 20, 2026
DOI: 10.53941/ams.2026.100001
Open original source ↗Source abstract
The Error Correcting Output Codes (ECOC) algorithm has played more and more important role in the artificial intelligence (AI) study and research fields in recent years due to its special properties and error correcting abilities. Various methods have been reported to improve its performance and classification accuracy. In this study, we developed a simplified ECOC encoding method to reduce the complexity in the encoding process and reduce its randomness in initializing the coding matrix. Furthermore, we also suggested a fuzzy mapping decoding (FMD) method used for the ECOC decoding process to improve its classification accuracy. By using this FMD, the classification accuracy can be improved up to 84% compared with the decoding method without using the FMD, which is 77%.
Evidence graph
No public relationships recorded yet.
Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.