Abstract
Classification on spatial data is different from classical classification in that spatial context must be taken into account. In particular, the validation criterion functions should incorporate both classification accuracy and spatial accuracy. However, direct combination of the two accuracies is cumbersome, due to their different subjects and scales. To circumvent this difficulty, we develop a new criterion function that indirectly incorporates spatial accuracy into classification accuracy-based functions. Next, we formally introduce a set of ideal properties that an appropriate criterion function should satisfy, giving a more meaningful interpretation for the relative significance coefficient in the weighted scheme. Finally, we compare the proposed new criterion function with existing ones on a large data set for 1980 US presidential election.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice-Hall (2002)
Chun, Y., Griffith, D.: Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology. SAGE (2013)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1137–1143 (1995)
Kim, J.: Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics & Data Analysis 53(11), 3735–3745 (2009)
Schiavo, R.A., Hand, D.J.: Ten more years of error rate research. International Statistical Review 68(3), 295–310 (2000)
Shekhar, S., Schrater, P., Raju, W.R., Wu, W., Chawla, S.: Spatial contextual classification and prediction models for mining geospatial data. IEEE Transactions on Multimedia 4(2), 174–188 (2002)
Chawla, S., Shekhar, S., Wu, W.: Predicting locations using map similarity (PLUMS):A framework for spatial data mining. In: Proceedings of the International Workshop on Multimedia Data Mining, pp. 14–24 (2000)
Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38, 2270–2285 (2005)
LeSage, J.P.: Bayesian estimation of spatial autoregressive models. International Regional Science Review 20, 113–129 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wei, X., Zhao, W., Li, Y. (2014). Approximate Distance Ranking-Based Validation for Spatial Contextual Classification: A Case Study of Election Data. In: Park, J., Pan, Y., Kim, CS., Yang, Y. (eds) Future Information Technology. Lecture Notes in Electrical Engineering, vol 309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55038-6_87
Download citation
DOI: https://doi.org/10.1007/978-3-642-55038-6_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55037-9
Online ISBN: 978-3-642-55038-6
eBook Packages: EngineeringEngineering (R0)