A review of improved extreme learning machine methods for data stream classification

被引：21

作者：

Li, Li ^{[1
]}

Sun, Ruizhi ^{[1
,2
]}

Cai, Saihua ^{[1
]}

Zhao, Kaiyi ^{[1
]}

Zhang, Qianqian ^{[1
]}

机构：

[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

[2] Minist Agr, Sci Res Base Integrated Technol Precis Agr Anim H, Beijing 100083, Peoples R China

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2019年 / 78卷 / 23期

关键词：

Data streams; Classification; Improved extreme leaning machine; Concept drifts; Imbalanced data streams; Uncertain data streams; INTRUSION DETECTION SYSTEM; DRIFTING DATA STREAMS; CLASS IMBALANCE; ONLINE; ENSEMBLE; ALGORITHM; UNCERTAIN; NETWORK; SCHEME; MODEL;

D O I：

10.1007/s11042-019-7543-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future.

引用

页码：33375 / 33400

页数：26

共 50 条

[21] Parallelized extreme learning machine for online data classification [J].

Vidhya, M. ;

Aji, S. .

APPLIED INTELLIGENCE, 2022, 52 (12) :14164-14177

[22] Extreme Learning Machine for Intent Classification of Web Data [J].

Parth, Yogesh ;

Wang Zhaoxia .

PROCEEDINGS OF ELM-2016, 2018, 9 :53-60

[23] Extreme Learning Machine for Two Category Data Classification [J].

Subbulakshmi, C. V. ;

Deepa, S. N. ;

Malathi, N. .

2012 IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES (ICACCCT), 2012, :458-461

[24] Spectra data classification with kernel extreme learning machine [J].

Zheng, Wenbin ;

Shu, Hongping ;

Tang, Hong ;

Zhang, Haiqing .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2019, 192

[25] A review on extreme learning machine [J].

Wang, Jian ;

Lu, Siyuan ;

Wang, Shui-Hua ;

Zhang, Yu-Dong .

MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (29) :41611-41660

[26] Active learning with extreme learning machine for online imbalanced multiclass classification [J].

Qin, Jiongming ;

Wang, Cong ;

Zou, Qinhong ;

Sun, Yubin ;

Chen, Bin .

KNOWLEDGE-BASED SYSTEMS, 2021, 231

[27] Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods [J].

Nosratabadi, Saeed ;

Mosavi, Amirhosein ;

Puhong Duan ;

Ghamisi, Pedram ;

Filip, Ferdinand ;

Band, Shahab S. ;

Reuter, Uwe ;

Gama, Joao ;

Gandomi, Amir H. .

MATHEMATICS, 2020, 8 (10) :1-25

[28] Incremental Learning for Classification of Unstructured Data Using Extreme Learning Machine [J].

Madhusudhanan, Sathya ;

Jaganathan, Suresh ;

Jayashree, L. S. .

ALGORITHMS, 2018, 11 (10)

[29] Quick extreme learning machine for large-scale classification [J].

Albtoush, Audi ;

Fernandez-Delgado, Manuel ;

Cernadas, Eva ;

Barro, Senen .

NEURAL COMPUTING & APPLICATIONS, 2022, 34 (08) :5923-5938

[30] Extreme Learning Machine as a Generalizable Classification Engine [J].

Zyarah, Abdullah M. ;

Kudithipudi, Dhireesha .

2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, :3371-3376

← 1 2 3 4 5 →