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2017 | OriginalPaper | Buchkapitel

Performance Metrics for Model Fusion in Twitter Data Drifts

verfasst von : Joana Costa, Catarina Silva, Mário Antunes, Bernardete Ribeiro

Erschienen in: Pattern Recognition and Image Analysis

Verlag: Springer International Publishing

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Abstract

Ensemble approaches have revealed remarkable abilities to tackle different learning challenges, namely in dynamic scenarios with concept drift, e.g. in social networks, as Twitter. Several efforts have been engaged in defining strategies to combine the models that constitute an ensemble. In this work, we investigate the effect of using different metrics for combining ensembles’ models, specifically performance-based metrics. We propose five performance combining metrics, having in mind that we may take advantage of diversity in classifiers, as their individual performance takes a leading role in defining their contribution to the ensemble. Experimental results on a Twitter dataset, artificially timestamped, suggest that using performance metrics to combine the models that constitute an ensemble can introduce relevant improvements in the overall ensemble performance.

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Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetCrossRefMATH

Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)MATH

Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefMATH

Bagul, R.D., Phulpagar, B.D.: Survey on approaches, problems and applications of ensemble of classifiers. Int. J. Emerg. Trends Technol. Comput. Sci. 5(1), 28–30 (2016)

Ditzler, G., Polikar, R.: Incremental learning of concept drift from streaming imbalanced data. IEEE Trans. Knowl. Data Eng. 25(10), 2283–2301 (2013)CrossRef

Tabassum, N., Ahmed, T.: A theoretical study on classifier ensemble methods and its applications. In: 3rd International Conference on Computing for Sustainable Global Development, pp. 67–78 (2016)

Ren, Y., Zhang, L., Suganthan, P.N.: Ensemble classification and regression - recent developments, applications and future directions. IEEE Comput. Intell. Mag. 1(1), 41–43 (2016)CrossRef

Ponti Jr., M.P.: Combining classifiers: from the creation of ensembles to the decision fusion. In: 24th Conference on Graphics, Patterns and Images, pp. 1–10 (2011)

Faria, E., de Carvalho, A., Gonçalves, I., Gama, J.: Novelty detection in data streams. Artif. Intell. Rev. 45(2), 235–269 (2016)CrossRef

10.

Kuncheva, L.: A theoretical study on six classifier fusion strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 281–286 (2002)CrossRef

11.

Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22, 1517–1531 (2011)CrossRef

12.

Karnick, M., Muhlbaier, M.D., Polikar, R.: Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach. In: International Conference on Pattern Recognition, pp. 1–4 (2008)

13.

Johnson, S.: How Twitter will change the way we live. Time Mag. 173, 23–32 (2009)

14.

Tsur, O., Rappoport, A.: What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the 5th International Conference on Web Search and Data Mining, pp. 643–652 (2012)

15.

Yang, L., Sun, T., Zhang, M., Mei, Q.: We know what @you #tag: does the dual role affect hashtag adoption? In: Proceedings of the 21st International Conference on World Wide Web, pp. 261–270 (2012)

16.

Chang, H.-C.: A new perspective on Twitter hashtag use: diffusion of innovation theory. In: Proceedings of the 73rd Annual Meeting on Navigating Streams in an Information Ecosystem, pp. 85:1–85:4 (2010)

17.

Costa, J., Silva, C., Antunes, M., Ribeiro, B.: Defining semantic meta-hashtags for Twitter classification. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 226–235. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37213-1_24 CrossRef

18.

Costa, J., Silva, C., Antunes, M., Ribeiro, B.: Choice of best samples for building ensembles in dynamic environments. In: Jayne, C., Iliadis, L. (eds.) EANN 2016. CCIS, vol. 629, pp. 35–47. Springer, Cham (2016). doi:10.1007/978-3-319-44188-7_3 CrossRef

19.

Costa, J., Silva, C., Antunes, M., Ribeiro, B.: The impact of longstanding messages in micro-blogging classification. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2015)

20.

Costa, J., Silva, C., Antunes, M., Ribeiro, B.: Concept drift awareness in Twitter streams. In: Proceedings of the 13th International Conference on Machine Learning and Applications, pp. 294–299 (2014)

21.

Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009)CrossRef

22.

Costa, J., Silva, C., Antunes, M., Ribeiro, B.: DOTS: drift oriented tool system. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9492, pp. 615–623. Springer, Cham (2015). doi:10.1007/978-3-319-26561-2_72 CrossRef

23.

Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1999)MATH

Titel: Performance Metrics for Model Fusion in Twitter Data Drifts
verfasst von: Joana Costa
Catarina Silva
Mário Antunes
Bernardete Ribeiro
Verlag: Springer International Publishing
Buch: Pattern Recognition and Image Analysis
Print ISBN: 978-3-319-58837-7

Electronic ISBN: 978-3-319-58838-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-58838-4_2

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