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Abstract

Data streams with class imbalance occur usually in many real applications. Online sequential learning is one of the effective methods for classifying data stream with class imbalance. This paper proposes a dual-weighted online sequential extreme learning machine (dw-ELM) method to solve it. On the basis of online sequential extreme learning machine, the proposed dw-ELM method analyzes the distribution characteristic of data in view of time and space, and gives an adaptive dual weighting scheme to tune the weights at both the time level and the space level. Extensive experimental evaluations on 10 imbalanced datasets indicate that the proposed dw-ELM method outperforms several comparing methods in terms of G-mean and F-measure metrics. Moreover, the proposed dw-ELM method remains superior classification performance in the presence of highly dynamic class imbalance.

Keywords

Data stream extreme learning machine online learning weight

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References

Huang

, Huang

G.B.

, Song

and You

, Trends in extreme learning machines: A review, Neural Networks 61 (2015), 32–48.

Krawczyk

, Stefanowski

and Wozniak

, Data stream classification and big data analytics, Neurocomputing 150 (2015), 238–239.

Domingos

and Hulten

, Mining High-speed Data Streams, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, pp. 71–80.

Hulten

, Spencer

and Domingos

, Mining Time-changing Data Streams, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 97–106.

Rusiecki

, Robust Neural Network for Novelty Detection on Data Streams, Proceedings of the 11th International Conference on Artificial Intelligence and Soft Computing, 2012, pp. 178–186.

Salperwyck

, Lemaire

and Hue

, Incremental Weighted Naive Bays Classifiers for Data Stream, In Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, 2015, pp. 179–190.

Bordes

and Bottou

, The Huller: A Simple and Efficient Online SVM, Proceedings of the 16th European Conference on Machine Learning, 2005, pp. 505–512.

Huang

G.B.

, Zhu

Q.Y.

and Siew

C.K.

, Extreme learning machine: Theory and applications, Neurocomputing 70 (2006), 489–501.

Liang

N.Y.

, Huang

G.B.

, Saratchandran

and Sundararajan

, A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17(6) (2006), 1411–1423.

10.

Nguyen

H.M.

, Cooper

E.W.

and Kamei

, Online Learning from Imbalanced Data Streams, Proceedings of International Conference of Soft Computing and Pattern Recognition, 2011, pp 347–352.

11.

Elwell

and Polikar

, Incremental learning of concept drift in non-stationary environments, IEEE Transactions on Neural Networks 22(10) (2011), 1517–1531.

12.

Ditzler

and Polikar

, An Ensemble based Incremental Learning Framework for Concept Drifts and Class Imbalance, Proceedings of the International Joint Conference on Neural Networks, 2010, pp. 1–8.

13.

Mirza

, Lin

and Toh

K.A.

, Weighted online sequential extreme learning machine for class imbalance learning, Neural Processing Letters 38 (2013), 465–486.

14.

Mirza

, Lin

and Liu

, Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift, Neurocomputing 149 (2015), 316–329.

15.

Specht

D.F.

, Probabilistic neural network, Neural Networks 3 (1990), 109–118.

16.

Parzen

, On estimation of a probability density function and mode, Annals of Mathematical Statistics 33 (1962), 1065–1076.

17.

Huang

G.B.

, Zhou

, Ding

and Zhang

, Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics 42(2) (2011), 513–529.

18.

Blake

C.L.

and Merz

C.J.

, UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Sciences, http://www.ics.uci.edu/∼mlearn/MLRepository.html

19.

Chawla

N.V.

, Bowyer

, Hall

and Kegelmeyer

, SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research 16 (2002), 231–357.

20.

López

, Fernández

and Herrera

, On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed, Information Sciences 257 (2014), 1–13.