Abstract It is problematic to classify data stream with imblanced class distributions for general online learning algorithms, especially in case of concept drift. In this paper, an adaptive weighted online extreme learning machine(AWO-ELM) is developed for imbalance data stream. AWO-ELM is an online learning method and it alleviates the class imbalance problem in chunk-by-chunk learning. Instead of adopting fixed weights, an efficient weight selection strategy is proposed to obtain better classification performance, and thus it can be applied to the task of learning static data stream with different imbalance ratio and the task of online learning with concept drift. The theoretical analysis and experimental results of several real data stream show that AWO-ELM obtains comparable or better classification performance than competing methods.
Fund:Supported by Natural Science Foundation of Zhejiang Province(No.LY18F030003), Foundation of High-Level Talents in Li-shui City(2017RC01)
About author:: (MEI Ying, master, associate professor. Her research interests include pattern recognition.) (LU Chengbo(Corresponding author), Ph.D., professor. His research interests include machine learning and data mining.)
[1] HE H B, GARCIA E A.Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284. [2] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357. [3] MALDONADO S, MONTECINOS C.Robust Classification of Imba-lanced Data Using One-Class and Two-Class SVM-Based Multiclassifiers. Intelligent Data Analysis, 2014, 18(1): 95-112. [4] ZHU W X, ZHONG P.A New One-Class SVM Based on Hidden Information. Knowledge-Based Systems, 2014, 60: 35-43. [5] ZONG W W, HUANG G B, CHEN Y Q. Weighted Extreme Lear-ning Machine for Imbalance Learning. Neurocomputing, 2013, 101: 229-242. [6] WANG S, MINKU L L, YAO X.A Learning Framework for Online Class Imbalance Learning // Proc of the IEEE Symposium on Computational Intelligence and Ensemble Learning. Washington, USA: IEEE, 2013: 36-45. [7] LU C B, KE H F, ZHANG G Y, ,et al. An Improved Weighted Extreme Learning Machine for Imbalanced Data Classification[J/OL]. [2018-06-25]. https://link.springer.com/content/pdf/10.. An Improved Weighted Extreme Learning Machine for Imbalanced Data Classification[J/OL]. [2018-06-25]. https://link.springer.com/content/pdf/10.1007%2Fs12293-017-0236-3.pdf. [8] NIU W J, FENG Z K, CHENG C T, et al. Forecasting Daily Runoff by Extreme Learning Machine Based on Quantum-Behaved Particle Swarm Optimization. Journal of Hydrologic Engineering, 2018, 23(3). DOI: 10.1061/(ASCE)HE.1943-5584.0001625. [9] ZHANG Y, WANG Y, ZHOU G X, et al. Multi-kernel Extreme Learning Machine for EEG Classification in Brain-Computer Interfaces. Expert Systems with Applications, 2018, 96: 302-310. [10] 刘阳阳,张骏,高欣健,等.基于卷积递归神经网络和核超限学习机的3D目标识别.模式识别与人工智能, 2017, 30(12): 1091-1099. (LIU Y Y, ZHANG J, GAO X J, et al. 3D Object Recognition via Convolutional-Recursive Neural Network and Kernel Extreme Learning Machine. Pattern Recognition and Artificial Intelligence, 2017, 30(12): 1091-1099.) [11] LIANG N Y, HUANG G B, SARATCHANDRAN P, et al. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. IEEE Transactions on Neural Networks, 2006, 17(6): 1411-1423. [12] MIRZA B, LIN Z P, TOH K A.Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning. Neural Processing Letters, 2013, 38(3): 465-486.
2019, Vol. 32 
