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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Approximate Distance Ranking-Based Validation for Spatial Contextual Classification: A Case Study of Election Data
verfasst von : Xiaorui Wei, Weiquan Zhao, Yangping Li
Erschienen in: Future Information Technology
Verlag: Springer Berlin Heidelberg
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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.