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Published in: New Generation Computing 2/2021

20-04-2021

Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data

Authors: Peipei Li, Man Wu, Junhong He, Xuegang Hu

Published in: New Generation Computing | Issue 2/2021

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Abstract

Data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlabeled data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class labels. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlabeled data.

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Literature
1.
go back to reference Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23, 69–101 (1996) Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23, 69–101 (1996)
2.
go back to reference Kolter, J.Z., Maloof, M.A., Domingos, P.: Dynamic weighted majority: a new ensemble method for tracking concept drift. In: Proceedings of ICDM’03, Melbourne, FL, United states, pp. 123–130 (2003) Kolter, J.Z., Maloof, M.A., Domingos, P.: Dynamic weighted majority: a new ensemble method for tracking concept drift. In: Proceedings of ICDM’03, Melbourne, FL, United states, pp. 123–130 (2003)
3.
go back to reference Ikonomovska, E., Gama, J., Džeroski, S.: Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150, 458–470 (2015) CrossRef Ikonomovska, E., Gama, J., Džeroski, S.: Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150, 458–470 (2015) CrossRef
4.
go back to reference Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of KDD’01, Boston, MA, United states, pp. 71–80 (2000) Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of KDD’01, Boston, MA, United states, pp. 71–80 (2000)
5.
go back to reference Domeniconi, C., Gunopulos, D.: Incremental support vector machine construction. In: Proceedings of ICDM’01, San Jose, CA, United states, pp. 589–592 (2001) Domeniconi, C., Gunopulos, D.: Incremental support vector machine construction. In: Proceedings of ICDM’01, San Jose, CA, United states, pp. 589–592 (2001)
6.
go back to reference Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Proceedings of SBIA Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, pp. 286–295 (2004) Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Proceedings of SBIA Brazilian Symposium on Artificial Intelligence, Sao Luis, Maranhao, Brazil, pp. 286–295 (2004)
7.
go back to reference Baena-Garća, M., del Campo-Avila, J., Fidalgo, R., Bifet, A., Gavaldà, R., Morales-Bueno, R.: Early drift detection method. In: Proceedings of the 4th international workshop on knowledge discovery from data streams, Berlin, Germany (2006) Baena-Garća, M., del Campo-Avila, J., Fidalgo, R., Bifet, A., Gavaldà, R., Morales-Bueno, R.: Early drift detection method. In: Proceedings of the 4th international workshop on knowledge discovery from data streams, Berlin, Germany (2006)
8.
go back to reference Zhang, P., Zhu, X., Tan, J., Guo, L.: Classifier and cluster ensembles for mining concept drifting data streams. In: Proceedings0 of ICDM’10, Sydney, NSW, Australia, pp. 1175–1180 (2010) Zhang, P., Zhu, X., Tan, J., Guo, L.: Classifier and cluster ensembles for mining concept drifting data streams. In: Proceedings0 of ICDM’10, Sydney, NSW, Australia, pp. 1175–1180 (2010)
9.
go back to reference Wu, X., Li, P., Hu, X.: Learning from concept drifting data streams with unlabeled data. Neurocomputing 92, 145–155 (2012) CrossRef Wu, X., Li, P., Hu, X.: Learning from concept drifting data streams with unlabeled data. Neurocomputing 92, 145–155 (2012) CrossRef
10.
go back to reference Masud, M.M., Woolam, C., Gao, J., Khan, L., Han, J., Hamlen, K.W., Oza, N.C.: Facing the reality of data stream classification: coping with scarcity of labeled data. Knowl. Inf. Syst. 33(1), 213–244 (2012) CrossRef Masud, M.M., Woolam, C., Gao, J., Khan, L., Han, J., Hamlen, K.W., Oza, N.C.: Facing the reality of data stream classification: coping with scarcity of labeled data. Knowl. Inf. Syst. 33(1), 213–244 (2012) CrossRef
11.
go back to reference Loo, H.R., Marsono, M.N.: Online data stream classification with incremental semi-supervised learning. In: Proceedings of CODS’15, Bangalore, India, pp. 132–133 (2015) Loo, H.R., Marsono, M.N.: Online data stream classification with incremental semi-supervised learning. In: Proceedings of CODS’15, Bangalore, India, pp. 132–133 (2015)
12.
go back to reference Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of KDD’01, San Francisco, CA, United states, pp. 97–106 (2001) Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of KDD’01, San Francisco, CA, United states, pp. 97–106 (2001)
13.
go back to reference Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014) CrossRef Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014) CrossRef
14.
go back to reference Gama, J., Sebastião, R., Holmes, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90, 317–346 (2013) MathSciNetCrossRef Gama, J., Sebastião, R., Holmes, P.P.: On evaluating stream learning algorithms. Mach. Learn. 90, 317–346 (2013) MathSciNetCrossRef
15.
go back to reference Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Moa: massive online analysis. Mach. Learn. Res. 11, 1601–1604 (2010) Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Moa: massive online analysis. Mach. Learn. Res. 11, 1601–1604 (2010)
16.
go back to reference Pears, R., Sakthithasan, S., Koh, Y.S.: Detecting concept change in dynamic data streams. Mach. Learn. 97(3), 259–293 (2014) MathSciNetCrossRef Pears, R., Sakthithasan, S., Koh, Y.S.: Detecting concept change in dynamic data streams. Mach. Learn. 97(3), 259–293 (2014) MathSciNetCrossRef
17.
go back to reference Almeida, E., Ferreira, C., Gama, J.: Learning model rules from high-speed data streams. In: Proceedings of IJCAI’13, Beijing, China, pp. 10–16 (2013) Almeida, E., Ferreira, C., Gama, J.: Learning model rules from high-speed data streams. In: Proceedings of IJCAI’13, Beijing, China, pp. 10–16 (2013)
18.
go back to reference Kpotufe, S., Orabona, F.: Regression-tree tuning in a streaming setting. In: Proceedings of NIPS’13, Lake Tahoe, NV, United states, pp. 1788–1796 (2013) Kpotufe, S., Orabona, F.: Regression-tree tuning in a streaming setting. In: Proceedings of NIPS’13, Lake Tahoe, NV, United states, pp. 1788–1796 (2013)
19.
go back to reference Shao, J., Ahmadi, Z., Kramer, S.: Prototype-based learning on concept-drifting data streams. In: Proceedings of KDD’14, New York, NY, United states, pp. 412–421 (2014) Shao, J., Ahmadi, Z., Kramer, S.: Prototype-based learning on concept-drifting data streams. In: Proceedings of KDD’14, New York, NY, United states, pp. 412–421 (2014)
20.
go back to reference Kosina, P., Gama, J.: Very fast decision rules for classification in data streams. Data Min. Knowl. Discov. 29(1), 168–202 (2015) MathSciNetCrossRef Kosina, P., Gama, J.: Very fast decision rules for classification in data streams. Data Min. Knowl. Discov. 29(1), 168–202 (2015) MathSciNetCrossRef
21.
go back to reference Mena-Torres, D., Aguilar-Ruiz, J.S.: A similarity-based approach for data stream classification. Expert Syst. Appl. 41(9), 4224–4234 (2014) CrossRef Mena-Torres, D., Aguilar-Ruiz, J.S.: A similarity-based approach for data stream classification. Expert Syst. Appl. 41(9), 4224–4234 (2014) CrossRef
22.
go back to reference Rosa, R.D., Orabona, F., Cesa-Bianchi, N.: The abacoc algorithm: a novel approach for nonparametric classification of data streams. In: Proceedings of ICDM’15, Atlantic City, NJ, United states, pp. 733–738 (2015) Rosa, R.D., Orabona, F., Cesa-Bianchi, N.: The abacoc algorithm: a novel approach for nonparametric classification of data streams. In: Proceedings of ICDM’15, Atlantic City, NJ, United states, pp. 733–738 (2015)
23.
go back to reference Frias-Blanco, I., del Campo-Avila, J., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., Caballero-Mota, Y.: Online and non-parametric drift detection methods based on Hoeffding’s bound. IEEE Trans. Knowl. Data Eng. 27(3), 810–823 (2015) CrossRef Frias-Blanco, I., del Campo-Avila, J., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., Caballero-Mota, Y.: Online and non-parametric drift detection methods based on Hoeffding’s bound. IEEE Trans. Knowl. Data Eng. 27(3), 810–823 (2015) CrossRef
24.
go back to reference Chen, D.Z., Yang, Q.L., Liu, J.M., Zeng, Z.: Selective prototype-based learning on concept-drifting data streams. Inf. Sci. 516, 20–32 (2020) CrossRef Chen, D.Z., Yang, Q.L., Liu, J.M., Zeng, Z.: Selective prototype-based learning on concept-drifting data streams. Inf. Sci. 516, 20–32 (2020) CrossRef
25.
go back to reference Kolter, J.Z., Maloof, M.A.: Using additive expert ensembles to cope with concept drift. In: Proceedings of machine learning, Bonn, Germany, pp. 449–456 (2005) Kolter, J.Z., Maloof, M.A.: Using additive expert ensembles to cope with concept drift. In: Proceedings of machine learning, Bonn, Germany, pp. 449–456 (2005)
26.
go back to reference Fan, W.: Systematic data selection to mine concept-drifting data streams. In: Proceedings of KDD’04, Seattle, WA, United states, pp. 128–137 (2004) Fan, W.: Systematic data selection to mine concept-drifting data streams. In: Proceedings of KDD’04, Seattle, WA, United states, pp. 128–137 (2004)
27.
go back to reference Sun, Y., Mao, G., Liu, X., Liu, C.: Mining concept drift from data streams based on multi-classifiers. Acta Automatica Sinica 34(1), 93–97 (2008) MathSciNetCrossRef Sun, Y., Mao, G., Liu, X., Liu, C.: Mining concept drift from data streams based on multi-classifiers. Acta Automatica Sinica 34(1), 93–97 (2008) MathSciNetCrossRef
28.
go back to reference Ramamurthy, S., Bhatnagar, R.: Tracking recurrent concept drift in streaming data using ensemble classifiers. In: Proceedings of international conference on machine learning and applications, Cincinnati, Ohio, pp. 404–409 (2007) Ramamurthy, S., Bhatnagar, R.: Tracking recurrent concept drift in streaming data using ensemble classifiers. In: Proceedings of international conference on machine learning and applications, Cincinnati, Ohio, pp. 404–409 (2007)
29.
go back to reference Li, P., Wu, X., Hu, X., Liang, Q., Gao, Y.: A random decision tree ensemble for mining concept drifts from noisy data streams. Appl. Artif. Intell. 24(7), 680–710 (2010) CrossRef Li, P., Wu, X., Hu, X., Liang, Q., Gao, Y.: A random decision tree ensemble for mining concept drifts from noisy data streams. Appl. Artif. Intell. 24(7), 680–710 (2010) CrossRef
30.
go back to reference Zhu, Q., Zhang, Y., Hu, X., Li, P.: A double-window-based classification algorithm for concept drifting data streams. Acta Automatica Sinica 37(9), 1077–1084 (2011) Zhu, Q., Zhang, Y., Hu, X., Li, P.: A double-window-based classification algorithm for concept drifting data streams. Acta Automatica Sinica 37(9), 1077–1084 (2011)
31.
go back to reference Bardda, J.P., Gomes, H.M., Enembreck, F.: Sfnclassifier: A scale-free social network method to handle concept drift. In: Proceedings of SAC’14, Gyeongju, Korea, Republic of, pp. 786–791 (2014) Bardda, J.P., Gomes, H.M., Enembreck, F.: Sfnclassifier: A scale-free social network method to handle concept drift. In: Proceedings of SAC’14, Gyeongju, Korea, Republic of, pp. 786–791 (2014)
32.
go back to reference Islam, M.R.: Recurring and novel class detection in concept-drifting data streams using class-based ensemble. In: Proceedings of PAKDD’14, Tainan, Taiwan, pp. 425–436 (2014) Islam, M.R.: Recurring and novel class detection in concept-drifting data streams using class-based ensemble. In: Proceedings of PAKDD’14, Tainan, Taiwan, pp. 425–436 (2014)
33.
go back to reference Zahra, A., Kramer, S.: Modeling recurring concepts in data streams: a graph-based framework. Knowl. Inf. Syst. 55, 1–30 (2017) Zahra, A., Kramer, S.: Modeling recurring concepts in data streams: a graph-based framework. Knowl. Inf. Syst. 55, 1–30 (2017)
34.
go back to reference Anderson, R., Koh, Y.S., Dobbie, G., Bifet, A.: Recurring concept meta-learning for evolving data streams. Expert Syst. Appl. 138, 112832 (2019) CrossRef Anderson, R., Koh, Y.S., Dobbie, G., Bifet, A.: Recurring concept meta-learning for evolving data streams. Expert Syst. Appl. 138, 112832 (2019) CrossRef
35.
go back to reference Chiu, C.W., Minku, L.L.: Diversity-based pool of models for dealing with recurring concepts. In: Proceedings of IJCNN’18 (2018) Chiu, C.W., Minku, L.L.: Diversity-based pool of models for dealing with recurring concepts. In: Proceedings of IJCNN’18 (2018)
36.
go back to reference Gomes, J.B., Gaber, M.M., Sousa, P.A.C., Menasalvas, E.: Mining recurring concepts in a dynamic feature space. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 95–110 (2014) CrossRef Gomes, J.B., Gaber, M.M., Sousa, P.A.C., Menasalvas, E.: Mining recurring concepts in a dynamic feature space. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 95–110 (2014) CrossRef
37.
go back to reference Sakthithasan, S., Pears, R., Bifet, A., Pfahringer, B.: Use of ensembles of fourier spectra in capturing recurrent concepts in data streams. In: Proceedings of IJCNN’15, Killarney, Ireland, pp. 1–8 (2015) Sakthithasan, S., Pears, R., Bifet, A., Pfahringer, B.: Use of ensembles of fourier spectra in capturing recurrent concepts in data streams. In: Proceedings of IJCNN’15, Killarney, Ireland, pp. 1–8 (2015)
38.
go back to reference Patil, P., Fatangare, Y., Kulkarni, P.: Semi-supervised learning algorithm for online electricity data streams. In: Proceedings of ICAEES’14, Kumaracoil, India, pp. 349–358 (2014) Patil, P., Fatangare, Y., Kulkarni, P.: Semi-supervised learning algorithm for online electricity data streams. In: Proceedings of ICAEES’14, Kumaracoil, India, pp. 349–358 (2014)
39.
go back to reference Sethi, T.S., Kantardzic, M., Hu, H.: A grid density based framework for classifying streaming data in the presence of concept drift. J. Intell. Inf. Syst. 46(1), 179–211 (2016) CrossRef Sethi, T.S., Kantardzic, M., Hu, H.: A grid density based framework for classifying streaming data in the presence of concept drift. J. Intell. Inf. Syst. 46(1), 179–211 (2016) CrossRef
40.
go back to reference Silva, C.A.S., Krohling, R.A.: Semi-supervised online elastic extreme learning machine with forgetting parameter to deal with concept drift in data streams. In: Proceedings of IJCNN’19, Budapest, Hungary (2019) Silva, C.A.S., Krohling, R.A.: Semi-supervised online elastic extreme learning machine with forgetting parameter to deal with concept drift in data streams. In: Proceedings of IJCNN’19, Budapest, Hungary (2019)
41.
go back to reference Ferreira, R.S., Zimbrao, G., Alvimb, L.G.M.: AMANDA: semi-supervised density-based adaptive model for non-stationary data with extreme verification latency. Inf. Sci. 488, 219–237 (2019) CrossRef Ferreira, R.S., Zimbrao, G., Alvimb, L.G.M.: AMANDA: semi-supervised density-based adaptive model for non-stationary data with extreme verification latency. Inf. Sci. 488, 219–237 (2019) CrossRef
42.
go back to reference Haque A., Khan L., Baron M.: SAND: Semi-supervised adaptive novel class detection and classification over data stream, In: 30th AAAI conference artificial intelligence, pp.1652–1658 (2016) Haque A., Khan L., Baron M.: SAND: Semi-supervised adaptive novel class detection and classification over data stream, In: 30th AAAI conference artificial intelligence, pp.1652–1658 (2016)
43.
go back to reference Din, S.U., Shao, J.M., Kumar, J.: Online reliable semi-supervised learning on evolving data streams. Inf. Sci. 525, 153–171 (2020) MathSciNetCrossRef Din, S.U., Shao, J.M., Kumar, J.: Online reliable semi-supervised learning on evolving data streams. Inf. Sci. 525, 153–171 (2020) MathSciNetCrossRef
44.
go back to reference Li, P.P., Wu, X.D., Hu, X.G.: Mining recurring concept drifts with limited labeled streaming data. ACM Trans. Intell. Syst. Technol. 3(2), 1–32 (2012) Li, P.P., Wu, X.D., Hu, X.G.: Mining recurring concept drifts with limited labeled streaming data. ACM Trans. Intell. Syst. Technol. 3(2), 1–32 (2012)
45.
go back to reference Gonçalves, P.M., Barros, R.S.M.: RCD: a recurring concept drift framework. Pattern Recognit. Lett. 39(4), 1018–1025 (2013) CrossRef Gonçalves, P.M., Barros, R.S.M.: RCD: a recurring concept drift framework. Pattern Recognit. Lett. 39(4), 1018–1025 (2013) CrossRef
46.
go back to reference Hosseini, M.J., Gholipour, A., Beigy, H.: An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams. Knowl. Inf. Syst. 46(3), 567–597 (2016) CrossRef Hosseini, M.J., Gholipour, A., Beigy, H.: An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams. Knowl. Inf. Syst. 46(3), 567–597 (2016) CrossRef
47.
go back to reference Ren, S.Q., Liao, B., Zhu, W., Can, F.: Knowledge-maximized ensemble algorithm for different types of concept drift. Inf. Sci. 430–431, 261–281 (2018) CrossRef Ren, S.Q., Liao, B., Zhu, W., Can, F.: Knowledge-maximized ensemble algorithm for different types of concept drift. Inf. Sci. 430–431, 261–281 (2018) CrossRef
48.
go back to reference Gama, J., Sebastião, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of KDD’09, Paris, France, pp. 329–337 (2009) Gama, J., Sebastião, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of KDD’09, Paris, France, pp. 329–337 (2009)
49.
go back to reference Liu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, Z.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346–2363 (2019) Liu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, Z.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346–2363 (2019)
50.
go back to reference Bifet, A.: Classifier concept drift detection and the illusion of progress. In: Artificial intelligence and soft computing, pp. 715–725. CRC Press, Boca Raton (2017) CrossRef Bifet, A.: Classifier concept drift detection and the illusion of progress. In: Artificial intelligence and soft computing, pp. 715–725. CRC Press, Boca Raton (2017) CrossRef
52.
go back to reference Severo, M., Gama, J.: Change detection with Kalman filter and CUSUM. In: Proceedings of international conference on discovery science, Barcelona, Spain, pp. 243–254 (2006) Severo, M., Gama, J.: Change detection with Kalman filter and CUSUM. In: Proceedings of international conference on discovery science, Barcelona, Spain, pp. 243–254 (2006)
53.
go back to reference Barros, R.S.M., Cabral, D.R.L., Goncalves, P.M., Santos, S.G.T.C.: RDDM: reactive drift detection method. Expert Syst. Appl. 90(C), 344–355 (2017) CrossRef Barros, R.S.M., Cabral, D.R.L., Goncalves, P.M., Santos, S.G.T.C.: RDDM: reactive drift detection method. Expert Syst. Appl. 90(C), 344–355 (2017) CrossRef
54.
go back to reference Gozuacik, O., Buyukcakir, A., Bonab, H., Can, F.: Unsupervised concept drift detection with a discriminative classifier. In: Proceedings of the 28th ACM international conference on information and knowledge management, Beijing, China (2019) Gozuacik, O., Buyukcakir, A., Bonab, H., Can, F.: Unsupervised concept drift detection with a discriminative classifier. In: Proceedings of the 28th ACM international conference on information and knowledge management, Beijing, China (2019)
55.
go back to reference Helmbold, D.P., Long, P.M.: Tracking drifting concepts by minimizing disagreement. Mach. Learn. 14, 27–45 (1994) MATH Helmbold, D.P., Long, P.M.: Tracking drifting concepts by minimizing disagreement. Mach. Learn. 14, 27–45 (1994) MATH
Metadata
Title
Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data
Authors
Peipei Li
Man Wu
Junhong He
Xuegang Hu
Publication date
20-04-2021
Publisher
Ohmsha
Published in
New Generation Computing / Issue 2/2021
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-021-00126-2

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