文献类型：期刊文献

英文题名：Incremental semi-supervised Extreme Learning Machine for Mixed data stream classification

作者：Li, Qiude Xiong, Qingyu Ji, Shengfen Yu, Yang Wu, Chao Gao, Min

第一作者：Li, Qiude

通信作者：Xiong, QY[1];Ji, SF[2]

机构：[1]Guizhou Med Univ, Sch Biol & Engn, Guiyang 550004, Guizhou, Peoples R China;[2]Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400044, Peoples R China;[3]Chongqing Univ, Sch Big Data & Software Engn, Chongqing 401331, Peoples R China;[4]Guizhou Inst Technol, Foreign Language Teaching Ctr, Guiyang 550003, Guizhou, Peoples R China

第一机构：Guizhou Med Univ, Sch Biol & Engn, Guiyang 550004, Guizhou, Peoples R China

通信机构：corresponding author), 55 Daxuecheng South Rd, Chongqing, Peoples R China;corresponding author), 1 Caiguan Rd, Guiyang, Guizhou, Peoples R China.

年份：2021

卷号：185

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20213210730456);Scopus(收录号：2-s2.0-85111803707);WOS:【SCI-EXPANDED(收录号:WOS:000707414500006)】；

基金：We thank anonymous reviewers for their valuable comments and suggestions. The work was supported by the Guizhou Provincial Natural Science Foundation, China (Grant No. ZK[2021]333), the PhD research startup foundation of Guizhou Medical University, China (Grant No. 2020-051), the Key Research Program of Chongqing Science & Technology Commission, China (Grant No. CSTC2019jscx-zdztzX0031 and CSTC2017jcyjBX0025), the Science and Technology Major Special Project of Guangxi, China (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).

语种：英文

外文关键词：Data stream classification; Extreme Learning Machine; Categorical data representation; Incremental learning

摘要：With an explosive growth of data generated in the Internet and other fields, the data stream classification has sparked broad interest recently. Nowadays, some of the challenges in data streams, such as concept drift detection and supervised data stream classification, have been well-developed. However, when confronted with mixed data streams (containing categorical and numerical values) or limited available labeled samples, many data stream methods cannot achieve a satisfying performance or even cannot work. To tackle these two problems, we proposed an Incremental Semi-supervised Extreme Learning Machine for Mixed data stream classification (MIS-ELM). To be specific, for the issue of mixed data in data streams, we designed a novel soft one-hot encoding method by combining the coupling object similarity method and the one-hot encoding method, which can embed categorical data into high-quality numerical data and is used in the data preprocessing phase of MIS-ELM; for the issue of limited labeled samples, we introduced an incremental learning method based on unlabeled data, which is employed in the training classifier phase of MIS-ELM. When no concept drift occurs in the data stream, MIS-ELM uses only unlabeled data for incremental learning to fine-tune the classifier trained in the previous sliding window. Also, MIS-ELM instinctively inherits the fast computability of ELM, so it is very suitable for the real-time processing of data streams. Finally, we evaluated the representation performance of the soft one-hot encoding and the classification performance of MIS-ELM, within real data streams. The experimental results demonstrate the superiority of the proposed methods over the state-of-the-art techniques in their areas, respectively.