Abstract
The progression of many biological and medical processes such as disease and development are inherently temporal in nature. However many datasets associated with such processes are from cross-section studies, meaning they provide a snapshot of a particular process across a population, but do not actually contain any temporal information. In this paper we address this by constructing temporal orderings of cross-section data samples using minimum spanning tree methods for weighted graphs. We call these reconstructed orderings pseudo time-series and incorporate them into temporal models such as dynamic Bayesian networks. Results from our preliminary study show that including pseudo temporal information improves classification performance. We conclude by outlining future directions for this research, including considering different methods for time-series construction and other temporal modelling approaches.
This is a preview of subscription content, log in via an institution to check access.
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
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning for examples. Technical Report, Univ. of Chicago, Department of Computer Science (TR-2004-06) (2004)
Booth, K.S., Lueker, G.S.: Testing for the consecutive ones property, interval graphs, and graph planarity using pq-tree algorithms. Journal of Computer and System Sciences (13), 335–379 (1976)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–163 (1997)
Friedman, N., Murphy, K.P., Russell, S.J.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the 14th Annual Conference on Uncertainty in AI, pp. 139–147 (1998)
Hand, D.J.: Construction and Assessment of Classification Rules. Wiley, Chichester (1997)
Heckerman, D., Geiger, D., Chickering, D.: Learning bayesian networks: The combination of knowledge and statistical data. In: KDD Workshop, pp. 85–96 (1994)
Hoch, I.: Estimation of production function parameters combining time-series and cross-section data. Econometrica 30(1), 34–53 (1962)
Magwene, P.M., Lizardi, P., Kim, J.: Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19(7), 842–850 (2003)
Patela, A.C., Markey, M.K.: Comparison of three-class classification performance metrics: a case study in breast cancer cad. 5749 (2005)
Rifkin, S.A., Kim, J.: Geometry of gene expression dynamics. Bioinformatics 18(9), 1176–1183 (2002)
Strauss, W.J., Carroll, R.J., Bortnick, S.M., Menkedick, J.R., Schultz, B.D.: Combining datasets to predict the effects of regulation of environmental lead exposure in housing stock. Biometrics 57(1), 203–210 (2001)
Tucker, A., Garway-Heath, D., Liu, X.: Bayesian classification and forecasting of visual field deterioration. In: The Proceedings of the Ninth Workshop on Intelligent Data Analysis in Medicine and Pharmacology and Knowledge-Based Information Management in Anaesthesia and Intensive Care (2003)
Tucker, A., Liu, X.: Learning dynamic bayesian networks from multivariate time series with changing dependencies. Intelligent Data Analysis– An International Journal 8(5), 469–480 (2004)
Tucker, A., ’t Hoen, P.A.C., Vinciotti, V., Liu, X.: Bayesian network classifiers for time-series microarray data. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 475–485. Springer, Heidelberg (2005)
Tucker, A., Vinciotti, V., Garway-Heath, D., Liu, X.: A spatio-temporal bayesian network classifier for understanding visual field deterioration. Artificial Intelligence in Medicine (34), 163–177 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peeling, E., Tucker, A. (2007). Making Time: Pseudo Time-Series for the Temporal Analysis of Cross Section Data. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-74825-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74824-3
Online ISBN: 978-3-540-74825-0
eBook Packages: Computer ScienceComputer Science (R0)