Abstract
The challenges raised by the massive data are being managed by the community through the advancements of infrastructure and algorithms, and now the processing of fast data is becoming a new hurdle to the researchers. Extreme Learning Machine (ELM) is a single-layer learning model with reliable performances and it is computationally simpler than the new generation deep architectures. ELM process the data in batches and the model has to be rerun while updates happening in the datasets. In the theoretical background of ELM, the past knowledge cannot be reused for improving the performance in online learning where the data set will be updated with mini-batches. In this paper, we have introduced a knowledge base to deal with the remembrance of knowledge in ELM. The architecture of the proposed model is designed to process mini-batches of any size to speed up the processing of the data on its arrival. A group of data sets with different properties such as sparse and feature dimensions is used in the experiments to evaluate our method. The performance of the algorithm is compared with a set of benchmarked classifiers and stream classifiers in the scikit-learn public platform. It is observed that our method could perform better in most of the experiments. It clear in the results that the Parallel ELM model outperformed the other methods in the training time across all the datasets. The consistent performance of our method shows the significance of parallel algorithms of ELM that can remember past knowledge.
Similar content being viewed by others
References
Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol 2. IEEE, pp 985–990
Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T (2010) Moa: Massive online analysis, a framework for stream classification and clustering. In: Proceedings of the first workshop on applications of pattern analysis. PMLR, pp 44–50
Dua D, Graff C (2017) UCI machine learning repository
Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010. Physica-Verlag HD, pp 177–186
Chang YW, Hsieh CJ, Chang KW, Ringgaard M, Lin CJ (2010) Training and testing low-degree polynomial data mappings via linear SVM. J Mach Learn Res, 11(4)
Yang X (2020) Introduction to stochastic calculus and its applications. Available at SSRN 3607647
Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1. IEEE, pp 278–282
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media, Berlin
Kégl B (2013) The return of AdaBoost. MH: multi-class Hamming trees. arXiv:1312.6086
John GH, Langley P (2013) Estimating continuous distributions in Bayesian classifiers. arXiv:1302.4964
Tharwat A (2016) Linear vs. quadratic discriminant analysis classifier: a tutorial. Int J Appl Pattern Recognit 3(2):145–180
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. (2011) Scikit-learn: Machine learning in Python. J Machine Learn Res 12:2825–2830
Kolter JZ, Maloof MA (2007) Dynamic weighted majority: an ensemble method for drifting concepts. J Machine Learn Res 8:2755–2790
Wang R, Chow CY, Lyu Y, Lee VC, Kwong S, Li Y, Zeng J (2017) Taxirec: recommending road clusters to taxi drivers using ranking-based extreme learning machines. IEEE Trans Knowl Data Eng 30(3):585–598
Wang H, Fan W, Yu PS, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp 226–235
Wang B, Pineau J (2016) Online bagging and boosting for imbalanced data streams. IEEE Trans Knowl Data Eng 28(12):3353–3366
Oza NC, Russell SJ (2001) Online bagging and boosting. In: International workshop on artificial intelligence and statistics. PMLR, pp 229–236
Montiel J, Read J, Bifet A, Abdessalem T (2018) Scikit-multiflow: A multi-output streaming framework. J Machine Learn Res 19(1):2915–2914
Vanschoren J, Van Rijn JN, Bischl B, Torgo L (2014) OpenML: networked science in machine learning. ACM SIGKDD Explorations Newsletter 15(2):49–60. https://doi.org/10.1145/2641190.2641198
Duan J, Ou Y, Hu J, Wang Z, Jin S, Xu C (2017) Fast and stable learning of dynamical systems based on extreme learning machine. IEEE Trans Syst Man Cybern Syst 49(6):1175–1185
Gomes HM, Bifet A, Read J, Barddal JP, Enembreck F, Pfharinger B, Holmes G, Abdessalem T (2017) Adaptive random forests for evolving data stream classification. Mach Learn 106(9):1469–1495
Kumar S, Banerjee B, Chaudhuri S (2021) Improved landcover classification using online spectral data hallucination. Neurocomputing 439:316–326
Dadkhah S, Shoeleh F, Yadollahi MM, Zhang X, Ghorbani AA (2021) A real-time hostile activities analyses and detection system. Applied Soft Computing 104:107175
Seraphim BI, Poovammal E (2021) Adversarial attack by inducing drift in streaming data. Wirel Pers Commun, 1–25
Li K, Luo G, Ye Y, Li W, Ji S, Cai Z (2020) Adversarial Privacy Preserving Graph Embedding against Inference Attack. IEEE Internet of Things Journal
Dong Y, Yang C, Zhang Y (2021) Deep metric learning with online hard mining for hyperspectral classification. Remote Sens 13(7):1368
Jo K, Kim J, Kim D, Jang C, Sunwoo M (2014) Development of autonomous car—Part I: Distributed system architecture and development process. IEEE Trans Ind Electron 61(12):7131–7140
Mirza B, Lin Z, Toh KA (2013) Weighted online sequential extreme learning machine for class imbalance learning. Neural Process Lett 38(3):465–486
Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892
Huang GB, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16-18):3460–3468
Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892
Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411– 1423
Yu H, Xie H, Yang X, Zou H, Gao S (2021) Online sequential extreme learning machine with the increased classes. Comput Electric Eng 90:107008
Wu C, Khishe M, Mohammadi M, Karim SHT, Rashid TA (2021) Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images. Soft Comput, 1–20
Rathore S, Park JH (2018) Semi-supervised learning based distributed attack detection framework for IoT. Appl Soft Comput 72:79–89
Zhang X, He T, Lu L, Yue S, Cheng D, Xu X (2017) Video analysis of traffic accidents based on projection extreme learning machine. In: 2017 international symposium on intelligent signal processing and communication systems (ISPACS). IEEE, pp 149–154
Ghomeshi H, Gaber MM, Kovalchuk Y (2020) A non-canonical hybrid metaheuristic approach to adaptive data stream classification. Futur Gener Comput Syst 102:127–139
Ghomeshi H, Gaber MM, Kovalchuk Y (2019) EACD: Evolutionary Adaptation to concept drifts in data streams. Data Min Knowl Disc 33(3):663–694
Liu W, Zhang H, Ding Z, Liu Q, Zhu C (2021) A comprehensive active learning method for multiclass imbalanced data streams with concept drift. Knowledge-Based Systems 215:106778
Li Z, Huang W, Xiong Y, Ren S, Zhu T (2020) Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm. Knowledge-Based Systems 195:105694
Baidari I, Honnikoll N (2020) Accuracy weighted diversity-based online boosting. Expert Systems with Applications 160:113723
Sarnovsky M, Kolarik M (2021) Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble. PeerJ Computer Science 7:e459
Museba T, Nelwamondo F, Ouahada K, Akinola A (2021) Recurrent adaptive classifier ensemble for handling recurring concept drifts. Applied Computational Intelligence and Soft Computing, 2021
Aydogdu O, Ekinci M (2020) A new approach for data stream classification: unsupervised feature representational online sequential extreme learning machine. Multimed Tools Appl 79(37):27205–27227
Lan Y, Soh YC, Huang GB (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72(13-15):3391–3395
Xu S, Wang J (2016) A fast incremental extreme learning machine algorithm for data streams classification. Expert Syst Appl 65:332–344
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
M, V., S, A. Parallelized extreme learning machine for online data classification. Appl Intell 52, 14164–14177 (2022). https://doi.org/10.1007/s10489-022-03308-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-022-03308-7