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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2014

01-02-2014 | Original Article

Online neural network model for non-stationary and imbalanced data stream classification

Authors: Adel Ghazikhani, Reza Monsefi, Hadi Sadoghi Yazdi

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2014

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Abstract

“Concept drift” and class imbalance are two challenges for supervised classifiers. “Concept drift” (or non-stationarity) is changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Previous methods for classifying non-stationary and imbalanced data streams mainly focus on batch solutions, in which the classification model is trained using a chunk of data. Here, we propose an online Neural Network (NN) model. The NN model, is composed of two different parts for handling concept drift and class imbalance. Concept drift is handled with a forgetting function and class imbalance is handled with a specific error function which assigns different importance to error in separate classes. The proposed method is evaluated on 3 synthetic and 8 real world datasets. The results show statistically significant improvement to previous online NN methods.
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Literature
1.
2.
Masud MM (2009) Adaptive classification of scarcely labeled and evolving data streams. Texas, Dallas
3.
Klinkenberg R, Joachims T (2000) Detecting concept drift with support vector machines. In: Paper presented at the 17th International conference on machine learning, San Mateo
4.
Sun J, Li H (2011) Dynamic financial distress prediction using instance selection for the disposal of concept drift. Expert Syst Appl 38(3):2566–2576CrossRef
5.
Martínez-Rego D, Pérez-Sánchez B, Fontenla-Romero O, Alonso-Betanzos A (2011) A robust incremental learning method for non-stationary environments. Neurocomputing 74(11):1800–1808CrossRef
6.
Pavlidis NG, Tasoulis DK, Adams NM, Hand DJ (2011) Landa perceptron: an adaptive classifier for data streams. Pattern Recogn 44(1):78–96CrossRefMATH
7.
Tsymbal A (2004) The problem of concept drift: definitions and related work. Technical Report: TCD-CS-2004-15. Trinity College Dublin, Computer Science Department, Dublin
8.
Elwell R, Polikar R (2011) Incremental learning of concept drift in nonstationary environments. IEEE Trans Neural Netw 22(10):1517–1531CrossRef
9.
Abdulsalam H, Skillicorn DB, Martin P (2011) Classification using streaming random forests. IEEE Trans Knowl Data Eng 23(1):22–36CrossRef
10.
Masud MM, Jing G, Khan L, Jiawei H, Thuraisingham BM (2011) Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans Knowl Data Eng 23(6):859–874CrossRef
11.
12.
Rodriguez JJ, Kuncheva LI (2008) Combining online classification approaches for changing environments. In: Paper presented at the Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, Orlando
13.
14.
Kuncheva LI (2004) Classifier ensembles for changing environments. In: Roli F, Kittler J, Windeatt T (eds) Multiple classifier systems. Lecture notes in computer science, vol 3077. Springer, Berlin, pp 1–15. doi:10.​1007/​978-3-540-25966-4_​1
15.
Kotsiantis S, Patriarcheas K, Xenos M (2010) A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowl-Based Syst 23(6):529–535CrossRef
16.
Abdelhamid B (2011) Incremental learning with multi-level adaptation. Neurocomputing 74(11):1785–1799CrossRef
17.
Pocock A, Yiapanis P, Singer J, Luján M, Brown G (2010) Online non-stationary boosting. In: El Gayar N, Kittler J, Roli F (eds) Multiple classifier systems. Lecture notes in computer science, vol 5997. Springer, Berlin, pp 205–214. doi:10.​1007/​978-3-642-12127-2_​21
18.
Minku L, Yao X (2011) DDD: a new ensemble approach for dealing with concept drift. IEEE Trans Knowl Data Eng 24(99):1–1
19.
Batuwita R, Palade V (2010) FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Trans Fuzzy Syst 18(3):558–571CrossRef
20.
Fernández A, del Jesus MJ, Herrera F (2010) On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets. Inf Sci 180(8):1268–1291CrossRef
21.
22.
Sánchez-Monedero J, Gutiérrez P, Fernández-Navarro F, Hervás-Martínez C (2011) Weighting efficient accuracy and minimum sensitivity for evolving multi-class classifiers. Neural Process Lett 34(2):101–116. doi:10.​1007/​s11063-011-9186-9 CrossRef
23.
Gao J, Fan W, Han J, Yu PS (2007) A general framework for mining concept-drifting data streams with skewed distributions. Paper presented at the SIAM
24.
Chen S, He H (2010) Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach. Evol Syst 2(1):35–50CrossRef
25.
Ditzler G, Polikar R (2010) An ensemble based incremental learning framework for concept drift and class imbalance. Paper presented at the WCCI
26.
Tong D, Mintram R (2010) Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cyber 1(1–4):75–87. doi:10.​1007/​s13042-010-0004-x CrossRef
27.
Boehm O, Hardoon D, Manevitz L (2011) Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. Int J Mach Learn Cyber 2(3):125–134. doi:10.​1007/​s13042-011-0030-3 CrossRef
28.
29.
Barakat M, Lefebvre D, Khalil M, Druaux F, Mustapha O (2013) Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues. Int J Mach Learn Cyber 4(3):217–233. doi:10.​1007/​s13042-012-0089-5 CrossRef
30.
Oh S-H (2011) Error back-propagation algorithm for classification of imbalanced data. Neurocomputing 74(6):1058–1061CrossRef
31.
Rumelhart DE, McClelland JL (1986) Parallel distributed processing. MIT Press, Cambridge
32.
Fontenla-Romero O, Guijarro-Berdiñas B, Pérez-Sánchez B, Alonso-Betanzos A (2010) A new convex objective function for the supervised learning of single-layer neural networks. Pattern Recogn 43(5):1984–1992CrossRefMATH
33.
Ghazikhani A, Monsefi R, Sadoghi Yazdi H (2012) Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams. Neural Comput Appl 1–13. doi:10.​1007/​s00521-012-1071-6
34.
He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284CrossRef
35.
Street NW, Kim Y (2001) A streaming ensemble algorithm (SEA) for large-scale classification. In: Paper presented at the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
36.
Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23:60–101
37.
Narasimhamurthy A, Kuncheva LI (2007) A framework for generating data to simulate changing environments. In: Paper presented at the IASTED International Conference on Artificial Intelligence and Applications
38.
Harries M (1999) Splice-2 comparative evaluation: electricity pricing. University of South Wales
39.
40.
41.
42.
Yang Y, Wu X, Zhu X (2006) Mining in anticipation for concept change: proactive-reactive prediction in data streams. Data Min Knowl Discov 13(3):261–289CrossRefMathSciNet
43.
Alpaydın E (2010) Introduction to machine learning, 2nd edn. The MIT Press, CambridgeMATH
44.
Sipser M (2006) Introduction to the theory of computation. Course Technology Inc, Boston
Metadata
Title
Online neural network model for non-stationary and imbalanced data stream classification
Authors
Adel Ghazikhani
Reza Monsefi
Hadi Sadoghi Yazdi
Publication date
01-02-2014
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2014
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0180-6

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