Many spatial phenomena are characterized by positive autocorrelation, i.e., variables take similar values at pairs of close locations. This property is strongly related to the smoothness assumption made in transductive learning, according to which if points in a high-density region are close, corresponding outputs should also be close. This observation, together with the prior availability of large sets of unlabelled data, which is typical in spatial applications, motivates the investigation of transductive learning for spatial data mining. The task considered in this work is spatial regression. We apply the co-training technique in order to iteratively learn two separate models, such that each model is used to make predictions on unlabeled data for the other. One model is built on the set of attribute-value observations measured at specific sites, while the other is built on the set of aggregated values measured for the same attributes in nearby sites. Experiments prove the effectiveness of the proposed approach on spatial domains.
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