Abstract
The characteristics of the data stream have brought enormous challenges to classification algorithms. Concept drift is the most concerning characteristics, and developed classification algorithms must tackle the concept drift problem. Therefore, Extreme Learning Machines (ELM) based algorithms have been developed to respond to the characteristics of the data stream. However, due to randomly assigned input layer weights, ELM based algorithms have encountered problems such as producing inconsistent outputs, generating ill-conditioned matrix, and mapping the inputs to the worst representative space. To overcome these problems, this paper aims to build a stable and well-constructed classifier that responds to the requirements of the data stream by considering all characteristics. A novel data stream classification approach based online sequential ELM (OS-ELM) with unsupervised feature representation learning (UFROS-ELM) and ensemble UFROS-ELM approach based on majority learning is presented in this paper. UFROS-ELM is a modification of the OS-ELM with ELM-AE and concept drift mechanism. ELM-AE is utilized for computing the best discriminative input weights of the classifier. The classifier is then initialized by using the determined weights, first chunk, and OS-ELM algorithm. When a new data chunk arrives, the classifier firstly searches any concept drift occurrence. If it is detected, ELM-AE is utilized to reconstruct the classifier to adapt to the changes. Otherwise, the classifier is sequentially updated updates by processing the current chunk. The results are achieved on the well-known real and artificial data sets and compared with state-of-the-art data stream classification algorithms. The experimental studies demonstrate the achievements of the proposed approaches.
Similar content being viewed by others
References
Aggarwal CC (2014) Data classification: algorithms and applications. CRC Press, Boca Raton
Amini A, Saboohi H, Ying wah t, Herawan T (2014) A fast density-based clustering algorithm for real-time internet of things stream. The Scientific World Journal. https://doi.org/10.1155/2014/926020
Arabmakki E, Kantardzic M (2017) SOM-based partial labeling of imbalanced data stream. Neurocomputing 262:120–133. https://doi.org/10.1016/j.neucom.2016.11.088
Armbrust M, Fox A, Griffith R (2010) A view of cloud computing. Commun ACM 53(4):50–58. https://doi.org/10.1145/1721654.1721672
Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Bengio Y, Lamblin P, Popovici D, Larochelle H (2006) Greedy layer-wise training of deep networks. NIPS’06 Proceedings of the 19th international conference on neural information processing systems 153–160
Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) Moa: Massive online analysis. J Mach Learn Res 11:1601–1604
Castaño A, Fernández-Navarro F, Hervás-Martínez C (2013) PCA-ELM: A robust and pruned extreme learning machine approach based on principal component analysis. Neur Process Lett 37(3):37–392
Deng W-Y, Ong Y-S, Tan PS, et al. (2016) Online sequential reduced kernel extreme learning machine. Neurocomputing 174:72–84. https://doi.org/10.1016/j.neucom.2015.06.087
Ding S, Mirza B, Lin Z, et al. (2018) Kernel based online learning for imbalance multiclass classification. Neurocomputing 277:139–148
Ding S, Zhang N, Zhang J, Xu X, Shi Z (2017) Unsupervised extreme learning machine with representational features. Int J Mach Learn Cyber 8(2):587–595
Dua D, Karra TE (2017) UCI machine learning repository
Fu Z, Sun X, Liu Q, Zhou L, Shu J (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans Commun E98(B(1)):190–200. https://doi.org/10.1587/transcom.E98.B.190
Han M, Liu XX (2014) An extreme learning machine algorithm based on mutual information variable selection. Control Decision 29(9):1576–1580
Han F, Yao HF, Ling QH (2013) An improved evolutionary extreme learning machine based on particle swarm optimization. Neurocomputing 116:87–93
Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cyber Part B (Cybernetics) 42(2):513–529
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1-3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Kasun LLC, Zhou H, Huang GB (2013) Representational learning with ELMs for big data. IEEE Intell Syst 28(6):31–34
Krawczyk B, Cano A (2018) Online ensemble learning with abstaining classifiers for drifting and noisy data streams. Appl Soft Comput 68:677–692. https://doi.org/10.1016/j.asoc.2017.12.008
Lall A, Sekar V, Ogihara M, Xu J, Zhang H (2006) Data streaming algorithms for estimating entropy of network traffic. ACM Sigmet Perform Eval Rev 34(1):145–156. https://doi.org/10.1145/1140103.1140295
Lan Y, Soh YC, Huang GB (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72(13-15):3391–3395
Laohakiat S, Phimoltares S, Lursinsap C (2017) A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction. Inf Sci 381:104–123
Li L, Sun R, Cai S, Zhao K, Zhang Q (2019) A review of improved extreme learning machine methods for data stream classification. Multimed Tools Appl 1–26 https://doi.org/10.1007/s11042-019-7543-2
Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neur Netw 17(6):1411–1423. https://doi.org/10.1109/TNN.2006.880583
Liu Z, Loo CK, Seera M (2019) Meta-cognitive recurrent recursive kernel OS-ELM for concept drift handling. Appl Soft Comput 75:494–507
Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2019) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neur Netw 21(1):158–162
Mirza B, Lin Z (2006) Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neur Netw 80:79–94
Mirza B, Lin Z, Liu N (2015) Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149:316–29
Mirza B, Lin Z, Toh KA (2013) Weighted online sequential extreme learning machine for class imbalance learning. Neural processing letters 38(3):465–486
Pacheco AG, Krohling RA, da Silva CA (2018) Restricted Boltzmann machine to determine the input weights for extreme learning machines. Expert Syst Appl 96:77–85
Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, New York
Rutkowski L, Pietruczuk L, Duda P, Jaworski M (2013) Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans Knowl Data Eng 25(6):1272–1279
Sethi TS, Kantardzic M (2017) On the reliable detection of concept drift from streaming unlabeled data. Expert Syst Appl 82:77–99
Shao Z, Er MJ (2016) An online sequential learning algorithm for regularized extreme learning machine. Neurocomputing 173:778–788. https://doi.org/10.1016/j.neucom.2015.08.029
Singh R, Kumar H, Singla RK (2015) An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Syst Appl 42(22):8609–8624
Tso F, Cui L, Zhang L (2013) Dragonnet: a robust mobile internet service system for long-distance trains. IEEE Trans Mob Comput 12(11):2206–2218. https://doi.org/10.1109/TMC.2012.191
Venkatesan R, Er MJ, Dave M, Pratama M, Wu S (2017) A novel online multi-label classifier for high-speed streaming data applications. Evolving Syst 8(4):303–315
Venkatesan R, Er MJ, Wu S, Pratama M (2016) A novel online real-time classifier for multi-label data streams. In: Proceedings International Joint Conference on Neural Network (IJCNN), Vancouver, BC Canada, pp 1833–1840
Wang Y, Cao F, Yuan Y (2011) A study on effectiveness of extreme learning machine. Neurocomputing 74(16):2483–2490. https://doi.org/10.1016/j.neucom.2010.11.030
Wang W, Liu X (2017) The selection of input weights of extreme learning machine: a sample structure preserving point of view. Neurocomputing 261:28–36
Wang D, Wang P, Ji Y (2015) An oscillation bound of the generalization performance of extreme learning machine and corresponding analysis. Neurocomputing 151:883–890
Webb GI, Hyde R, Cao H, Nguyen HL, Petitjean F (2016) Characterizing concept drift. Data Min Knowl Disc 30(4):964–994. https://doi.org/10.1007/s10618-015-0448-4
Xiao D, Li B, Zhang S (2018) An online sequential multiple hidden layers extreme learning machine method with forgetting mechanism. Chemometr Intell Lab Syst 176:126–133
Xu S, Wang J (2016) A fast incremental extreme learning machine algorithm for data streams classification. Expert Syst Appl 65:332–344. https://doi.org/10.1016/j.eswa.2016.08.052
Xu S, Wang J (2017) Dynamic extreme learning machine for data stream classification. Neurocomputing 238:433–449
Yang R, Xu S, Feng L (2018) An ensemble extreme learning machine for data stream classification. Algorithms 11(7):107
Yu H, Webb GI (2019) Adaptive online extreme learning machine by regulating forgetting factor by concept drift map. Neurocomputing 343:141–153
Zeng XQ, Li GZ (2014) Incremental partial least squares analysis of big streaming data. Pattern Recogn 47(11):3726–3735. https://doi.org/10.1016/j.patcog.2014.05.022
Zeng Y, Qian L, Ren J (2018) Evolutionary hierarchical sparse extreme learning autoencoder network for object recognition. Symmetry 10(10):474
Zhang Y, Liu W, Ren X, et al. (2017) Dual weighted extreme learning machine for imbalanced data stream classification. J Intell Fuzzy Syst 33 (2):1143–1154
Zhang P, Zhu X, Shi Y, Guo L, Wu X (2011) Robust ensemble learning for mining noisy data streams. Decis Support Syst 50(2):469–479
Zhao G, Shen Z, Man Z (2011) Robust input weight selection for well-conditioned extreme learning machine. Int J Inf Technol 17(1):1–13
Zhao J, Wang Z, Park DS (2012) Online sequential extreme learning machine with forgetting mechanism. Neurocomputing 87:79–89
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
Aydogdu, O., Ekinci, M. A new approach for data stream classification: unsupervised feature representational online sequential extreme learning machine. Multimed Tools Appl 79, 27205–27227 (2020). https://doi.org/10.1007/s11042-020-09300-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09300-y