文献类型：期刊文献

英文题名：A method for mixed data classification base on RBF-ELM network

作者：Li, Qiude Xiong, Qingyu Ji, Shengfen Yu, Yang Wu, Chao Yi, Hualing

第一作者：Li, Qiude

通信作者：Xiong, QY[1]

机构：[1]Chongqing Univ, Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China;[2]Chongqing Univ, Sch Big Data & Software Engn, Chongqing 401331, Peoples R China;[3]Guizhou Med Univ, Sch Biol & Engn, Guiyang 550025, Guizhou, Peoples R China;[4]Guizhou Inst Technol, Sch Foreign Language, Guiyang 550003, Guizhou, Peoples R China

第一机构：Chongqing Univ, Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China

通信机构：corresponding author), Chongqing Univ, Minist Educ, Key Lab Dependable Serv Comp Cyber Phys Soc, Chongqing, Peoples R China.

年份：2021

卷号：431

起止页码：7-22

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20210209751363);Scopus(收录号：2-s2.0-85098994693);WOS:【SCI-EXPANDED(收录号:WOS:000618958800002)】；

基金：We thank anonymous reviewers for their valuable comments and suggestions. The work was supported by the Key Research Program of Chongqing Science & Technology Commission (Grant No. CSTC 2019jscx-zdztzX0031 and CSTC2017jcyjBX0025), the PhD research startup foundation of Guizhou Medical University (Grant No. 2020-051), the Science and Technology Major Special Project of Guangxi (Grant No. GKAA17129002), the National Natural Science Foundations of China (Grant No. 61771077), and the National Key R&D Program of China (Grant No. 2018YFF0214706), Graduate Scientific Research and Innovation Foundation of Chongqing (Grant No. CYB19072).

语种：英文

外文关键词：Mixed data classification; Distance metric; Density peaks clustering (DPC); Radial basis function (RBF); Extreme learning machine (ELM)

摘要：The classification tasks for numerical or categorical data have been well developed. However, the data collected in the real world are frequently the mixed type containing numerical and categorical values, and how to classify the mixed data quickly and efficiently is a critical yet challenging task. Existing classification models for mixed data usually treat the mixed data processing and subsequent classification as two independent phases, without considering their compatibility. By fusing the mixed data processing into a classification algorithm, this paper proposes an extended version of RBF-ELM (Radial Basis Function-Extreme Learning Machine), a Mixed Data RBF-ELM method (MD-RBF-ELM for short), which can achieve direct, fast, and efficient classification for mixed data. Specifically, a distance metric method for mixed data is firstly designed to calculate the distances between the input data and the RBF centers, and then these distances are used to train the network structure and weights of MD-RBF-ELM, thereby realizing the fusion of data processing with model learning. In addition, to alleviate the problem of MD-RBF-ELM's unstable performance caused by randomly selecting the RBF centers, we propose an improved density peak clustering algorithm and use it to select the optimal RBF centers automatically and adaptively. Extensive experimental results on 34 data sets demonstrate that MD-RBF-ELM significantly enhances the classification performance (increasing 2.37% for F1-score, up to 14/34 for the number of best results, and reaching 2.4/8 for the averaged ranks), compared with seven state-of-the-art competitors. (C) 2020 Elsevier B.V. All rights reserved.