文献类型：期刊文献

英文题名：A meta-learning RBF-ELM network with a novel conditional entropy distance metric for mixed-attribute data classification

作者：Li, Qiude Wang, Liezhen Ji, Shengfen Yu, Yang Hu, Sigui Xiong, Qingyu Hu, Zuquan Zeng, Zhu

第一作者：Li, Qiude

通信作者：Li, QD[1];Hu, ZQ[1];Zeng, Z[1]

机构：[1]Guizhou Med Univ, Sch Biol & Engn, Guizhou Key Lab Microbio & Infect Dis Prevent & Co, Guiyang, Peoples R China;[2]Guizhou Inst Technol, Sch Foreign Language, Guiyang, Peoples R China;[3]Chongqing Univ, Sch Big Data & Software Engn, Chongqing, Peoples R China

第一机构：Guizhou Med Univ, Sch Biol & Engn, Guizhou Key Lab Microbio & Infect Dis Prevent & Co, Guiyang, Peoples R China

通信机构：corresponding author), Guizhou Med Univ, Sch Biol & Engn, Guizhou Key Lab Microbio & Infect Dis Prevent & Co, Guiyang, Peoples R China.

年份：2026

卷号：38

期号：6

外文期刊名：JOURNAL OF KING SAUD UNIVERSITY COMPUTER AND INFORMATION SCIENCES

收录：;Scopus(收录号：2-s2.0-105047413981);WOS:【SCI-EXPANDED(收录号:WOS:001850294100001)】；

基金：The work was supported by the Guizhou Provincial Basic Research Program (Natural Science) (Grant No. MS[2025]553, QKHJC-[2024]Youth 226, MS[2026]543), the Cultivation Project of National Natural Science Foundation of China at Guizhou Medical University (No. 25NSFCP20), the National Natural Science Foundation of China (Grant No. 62166009, 12132006), the Guizhou Key Laboratory of Microbio and Infectious Disease Prevention & Control (ZDSYS[2023]004), the Excellent Young Talents Plan of Guizhou Medical University (2021-101), and Science and Technology Foundation of Guizhou Provincial Health Commission (Grant No. gzwkj2023-258, 025GZWJKJXM0914).

语种：英文

外文关键词：Mixed-attribute data; RBF-ELM classifier; Distance metric; Meta-learning; Conditional entropy

摘要：Classification methods for mixed-attribute data typically adopt a two-stage strategy, which first either converts such data into a uniform type or constructs a distance metric, and then applies the result to downstream classification models. However, these methods overlook the inherent ordering of ordinal categorical values during the first stage data processing or distance metric, and their two stages are handled separately without interaction, exhibiting insufficient compatibility. To address these issues, this paper extends the RBF-ELM (Radial Basis Function - Extreme Learning Machine) classifier to directly (without stage-wise) handle mixed-attribute data containing ordinal attributes, and proposes a Meta-Learning-driven RBF-ELM classifier (ML-RBF-ELM for short). Specifically, by mining the associations between class labels and categorical attributes (including nominal and ordinal types), we design a novel conditional entropy-based distance metric to compute the distances between input samples and kernel centers in RBF-ELM, thereby extending RBF-ELM into a classification model capable of directly processing mixed-attribute data. Furthermore, to effectively select RBF kernel centers, we employ a meta-learning method to adaptively determine the optimal hyperparameter k in k-prototypes clustering, and subsequently select the optimal RBF kernel centers. Experimental results on 30 mixed-attribute datasets demonstrate that our ML-RBF-ELM achieves a favorable performance trade-off between classification accuracy and computational efficiency compared with nine state-of-the-art competing methods.