att_abstract={{This paper presents an expository study of temporal data mining for
prediction of a future response sequence via mining large number of highly correlated
concurrent time series. In the study, we investigate a two dimensional search
scheme over time domain weighting and feature space selection. The weighting
of observation records over time domain is used to exploit the time dependency
structure and feature space selection is enforced to avoid the over-fitting issue.
For a specific temporal and spatial selection, its area under ROC curve (AUC)
is used to evaluate the prediction performance over the training and testing data.
By varying the weighting scheme and feature selection, AUC contour maps on
both training and testing data are generated. The contour maps can suggest us to
apply the optimal allocations with highest AUC for future responses prediction in
training, testing , and possible validation data. Numerical results over two sets of
temporal data with different applications have shown that the proposed scheme
can improve the prediction performance of conventional data mining methods
	att_authors={sa2858, gm1461, rw218j},
	att_copyright={{IAENG International Association of Engineers (ISBN: 978-988-18210-3-4) }},
	att_copyright_notice={{The definitive version was published in The International MultiConference of Engineers and Computer Scientists 2011. {{, 2012-03-01}}}},
	author={Siu-tong Au and Guang-qin Ma and Rensheng Wang},
	institution={{IAENG International Association of Engineers}},
	title={{An Optimal Temporal and Feature Space Allocation in Supervised Data Mining }},