Abstract
Soft sensing is a class of problems that aim to sense something of interest that cannot be measured directly through something else that can be measured directly. The problems are usually studied as separate topics in different fields, and there is little research studying these problems in a unified fashion. In this paper we argue that there are commonalities among these problems. They can all be formulated as class-imbalanced binary classification problems. We present an extension of Lattice Machine, which is binary classification and by focusing on characterising positive class to deal with class-imbalanced binary classification problems. We also present experimental results, where some public data sets from UCI data repository are turned into binary-class data and consequently they become class-imbalanced. These experiments show that the extended Lattice Machine outperforms the popular machine learning algorithms (SVM, NN, decision tree induction) when used as soft sensing engines, in terms of precision.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berg, T., Belhumeur, P.N.: Tom-vs-pete classifiers and identity-preserving alignment for face verification. In: BMVC, vol. 2, p. 7. Citeseer (2012)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41(3), 15 (2009)
Harding, G., Lanza, R.C., Myers, L.J., Young, P.A.: Substance detection systems. In: Substance Detection Systems, vol. 2092 (1994)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6(1), 1–6 (2004)
Zhou, Z.-H.: Cost-sensitive learning. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds.) MDAI 2011. LNCS, vol. 6820, pp. 17–18. Springer, Heidelberg (2011)
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 34(3), 334–352 (2004)
Opelt, A., Fussenegger, M., Pinz, A., Auer, P.: Weak hypotheses and boosting for generic object detection and recognition. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 71–84. Springer, Heidelberg (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Vu, V.-T., Bremond, F., Thonnat, M.: Automatic video interpretation: A novel algorithm for temporal scenario recognition. IJCAI 3, 1295–1300 (2003)
Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1473–1488 (2008)
Delac, K., Grgic, M.: A survey of biometric recognition methods. In: Proceedings of 46th International Symposium on Electronics in Marine, Elmar 2004, pp. 184–193. IEEE (2004)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263–1284 (2009)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. arXiv preprint arXiv:1106.1813 (2011)
Liu, X.-Y., Wu, J., Zhou, Z.-H.: Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2), 539–550 (2009)
Sun, Y., Kamel, M.S., Wong, A.K., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40(12), 3358–3378 (2007)
Wang, H., Dubitzky, W., Düntsch, I., Bell, D.: A lattice machine approach to automated casebase design: Marrying lazy and eager learning. IJCAI, 254–263 (1999)
Wang, H., Düntsch, I., Gediga, G., Skowron, A.: Hyperrelations in version space. In: Proceedings of the 2002 ACM Symposium on Applied Computing, pp. 514–518. ACM (2002)
Wang, H., Düntsch, I., Trindade, L.: Lattice machine classification based on contextual probability. Fundamenta Informaticae 127(1), 241–256 (2013)
Blake, C., Merz, C.J.: Uci repository of machine learning databases. irvine, ca: University of california. Department of Information and Computer Science, vol. 55 (1998), http://www.ics.uci.edu/~mlearn/mlrepository.html
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wan, H., Wang, H., Guo, G., Lin, S. (2014). Soft Sensing as Class-Imbalance Binary Classification – A Lattice Machine Approach. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. UCAmI 2014. Lecture Notes in Computer Science, vol 8867. Springer, Cham. https://doi.org/10.1007/978-3-319-13102-3_85
Download citation
DOI: https://doi.org/10.1007/978-3-319-13102-3_85
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13101-6
Online ISBN: 978-3-319-13102-3
eBook Packages: Computer ScienceComputer Science (R0)