Abstract
Novelty detection is an important topic in data stream classification, as it is responsible for identifying the emergence of new concepts, new patterns, and outliers. It becomes necessary when the true label of an instance is not available right after its classification. The time between the classification of an instance and the arrival of its true label is called latency. This is a common scenario in data streams applications. However, most classification algorithms do not consider such a problem and assume that there will be no latency. On the other hand, a few methods in the literature cope with the existence of infinite latency and novelty detection in data streams. In this work, however, we focus on the scenario where the true labels will be available to the system after a certain time, called intermediate latency. Such a scenario is present in the stock market and weather datasets. Moreover, aiming for more flexible learning to deal with the uncertainties inherent in data streams, we consider the use of fuzzy set theory concepts. Therefore, we propose a method for classification and novelty detection in data streams called Fuzzy Classifier with Novelty Detection for data streams (FuzzCND). Our method uses an ensemble of fuzzy decision trees to perform the classification of new instances and applies the concepts of fuzzy set theory to detect possible novelties. The experiments showed that our approach is promising in dealing with the emergence of new concepts in data streams and inaccuracies in the data.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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- 1.
Available at https://github.com/andrecristiani/.
References
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (2002)
de Faria, E.R., Ponce de Leon Ferreira Carvalho, A.C., Gama, J.: MINAS: multiclass learning algorithm for novelty detection in data streams. Data Min. Knowl. Discov. 30(3), 640–680 (2015). https://doi.org/10.1007/s10618-015-0433-y
Souza, V., Pinho, T., Batista, G.: Evaluating stream classifiers with delayed labels information. In: 7th Brazilian Conference on Intelligent Systems (BRACIS) (2018)
Harries, M.: SPLICE-2 comparative evaluation: electricity pricing. Technical report 1, p. 6, University of New South Wales, Sydney, Australia (1999)
Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011). https://doi.org/10.1109/TNN.2011.2160459
Škrjanc, I., Iglesias, J.A., Sanchis, A., Leite, D., Lughofer, E., Gomide, F.: Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: a survey. Inf. Sci. 490, 344–368 (2019)
Cintra, M.E., Monard, M.C., Camargo, H.A.: FuzzyDT – a fuzzy decision tree algorithm based on C4.5. In: Proceedings of the Brazilian Congress on Fuzzy Systems (2012)
Bifet, A., Gavaldà, R.: Adaptive learning from evolving data streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249–260. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03915-7_22
da Silva, T.P.: Abordagem Fuzzy para Detecção de Novidade em Fluxo Contínuo de Dados. Dissertação de mestrado. Universidade Federal de São Carlos (2018)
Masud, M., Gao, J., Khan, L., Han, J., Thuraisingham, B.M.: Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans. Knowl. Data Eng. 23(6), 859–874 (2011)
Gomes, H.M., et al.: Adaptive random forests for evolving data stream classification. Mach. Learn. 106, 1469–1495 (2017). https://doi.org/10.1007/s10994-017-5642-8
Souza, V.M.A., Silva, D.F., Gama, J., Batista, G.E.: Data stream classification guided by clustering on nonstationary environments and extreme verification latency. In: Proceedings of the 2015 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2015)
da Silva, T.P., Souza, V.M.A., Batista, G.E.A.P.A., de Arruda Camargo, H.: A fuzzy classifier for data streams with infinitely delayed labels. In: Vera-Rodriguez, R., Fierrez, J., Morales, A. (eds.) CIARP 2018. LNCS, vol. 11401, pp. 287–295. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13469-3_34
Al-Khateeb, T., Masud, M.M., Khan, L., Aggarwal, C., Han, J., Thuraisingham, B.: Stream classification with recurring and novel class detection using class-based ensemble. In: 12th International Conference on Data Mining, Brussels, pp. 31–40 (2012). https://doi.org/10.1109/ICDM.2012.125
Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005). https://doi.org/10.1109/TFUZZ.2004.840099
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Wožniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017)
Campello, R.J.G.B., Hruschka, E.R.: A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets Syst. 157(21), 2858–2875 (2006)
Xiong, X., Chan, K.L., Tan, K.L.: Similarity-driven cluster merging method for unsupervised fuzzy clustering. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. AUAI Press (2004)
Gao, J., Fan, W., Han, J.: On appropriate assumptions to mine data streams: analysis and practice. In: Seventh IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, pp. 143–152 (2007)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning, 1st edn. Morgan Kaufmann, Burlington (1993)
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Cristiani, A.L., da Silva, T.P., de Arruda Camargo, H. (2020). A Fuzzy Approach for Classification and Novelty Detection in Data Streams Under Intermediate Latency. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12320. Springer, Cham. https://doi.org/10.1007/978-3-030-61380-8_12
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