Abstract
With the development of Internet of Vehicles (IOV), data mining on vehicle running status data has been a hot field of research, and the data quality has an important effect to the result of data mining. In this paper, we have investigated the problem of multidimensional analysis of vehicle running state data, especially the abnormal fuel-level data. In order to screen out the vehicles with abnormal sensors or equipments and evaluate the credibility of the data, we propose a bayesian classification algorithm to efficiently assess the data quality and screen out the abnormal vehicles in our database, with coefficient of variance (COV) and dispensation (COD) as feature attributes. Moreover, the accuracy indicators F-score and PPV of the classifier are used to determine the optimal threshold of the classifier. Our experiments on large real datasets show the feasibility and practical utility of proposed methods.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ahn, K., Rakha, H., Trani, A., Van Aerde, M.: Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J. Transp. Eng. 128(2), 182–190 (2002)
Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2009)
Beitzel, S.M.: On understanding and classifying web queries. Ph.D. thesis, Citeseer (2006)
Bishop, C.: Pattern recognition and machine learning. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 16(4), 049901 (2006). Springer
Biswas, S., Tatchikou, R., Dion, F.: Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety. IEEE Commun. Mag. 44(1), 74–82 (2006)
Christopher, M.B.: Pattern Recognition and Machine Learning. Company New York 16(4), 049901 (2006)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning (2012). http://Kioloa08.mlss.cc
Ni, J., Zhang, C., Yang, S.X.: An adaptive approach based on KPCA and SVM for real-time fault diagnosis of HVCBS. IEEE Trans. Power Delivery 26(3), 1960–1971 (2011)
Rodgers, J.L., Nicewander, W.A.: Thirteen ways to look at the correlation coefficient. Am. Stat. 42(1), 59–66 (2012)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, Pearson education/Addison. Server (Part 26(25)), 236–238 (2006)
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Mach. Learn. 42(3), 203–231 (2001)
Tang, L.A., Yu, X., Kim, S., Han, J., Peng, W.C., Sun, Y., Gonzalez, H., Seith, S.: Multidimensional analysis of a typical events in cyber-physical data. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1025–1036 (2012)
Metsis, V.: Spam filtering with naive bayes - which naive bayes?. In: Third Conference on Email and Anti-Spam (CEAS) (2006). Telecommunications
Zemmoudj, S., Kemmouche, A., Chibani, Y.: Feature selection and classification for urban data using improved F-score with support vector machine. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 371–375. IEEE (2014)
Zhang, H.: The optimality of naive bayes. Int. J. Pattern Recogn. Artif. Intell. 19(2), 183–198 (2005)
Acknowledgments
This research is supported by the National Natural Science Foundation of China under Grant No. 91118008, and the Foundation of Key Laboratory of Road and Traffic Engineering of the Ministry of Education in Tongji University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tian, D., Zhu, Y., Xia, H., Wang, J., Liu, H. (2015). A Quality Analysis Method for the Fuel-level Data of IOV. In: Hsu, CH., Xia, F., Liu, X., Wang, S. (eds) Internet of Vehicles - Safe and Intelligent Mobility. IOV 2015. Lecture Notes in Computer Science(), vol 9502. Springer, Cham. https://doi.org/10.1007/978-3-319-27293-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-27293-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27292-4
Online ISBN: 978-3-319-27293-1
eBook Packages: Computer ScienceComputer Science (R0)