Solving Data Overlapping Problem Using A Class-Separable Extreme Learning Machine Auto-Encoder

作     者：Boonchieng, Ekkarat Nadda, Wanchaloem 

作者机构：Chiang Mai Univ Fac Sci Dept Comp Sci Chiang Mai 50200 Thailand Khon Kaen Univ Coll Comp Khon Kaen 40002 Thailand 

出 版 物：《ADVANCED INTELLIGENT SYSTEMS》 (Adv. Intell. Syst.)

年 卷 期：2025年第7卷第3期

核心收录：

基　　金：Chiang Mai University Fundamental Fund 2025, Chiang Mai University 

主　　题：auto-encoding data classification data overlapping extreme learning machine auto-encoding imbalanced data 

摘      要：Data overlapping and imbalanced data are significant challenges in data classification. Extreme learning machine auto-encoding (ELM-AE) is a feature reduction method that transforms original features into a new set of features capturing essential information in the data. However, ELM-AE may not effectively solve the overlapping data problem. In this research, a new method called class-separable extreme learning machine auto-encoding (CS-ELM-AE) is proposed, to improve ELM-AE s efficacy in addressing the overlapping data problem and thereby increasing classification efficiency. CS-ELM-AE encodes points in the same class of the dataset to be closer together. Oversampling is also applied to the encoded dataset to solve the imbalanced data problem. The experiments demonstrate that CS-ELM-AE could significantly improve classification model performance and achieve higher levels of accuracy, as well as greater f1-score and G-mean values than the original ELM-AE.