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The Classification of Imbalanced Spatial Data

This paper describes a method of improving the prediction of urbanization. The four datasets used in this study were extracted using Geographical Information Systems (GIS). Each dataset contains seven independent variables related to urban development and a class label which denotes the urban areas versus the rural areas. Two classification methods Support Vector Machines (SVM) and Neural Networks (NN) were used in previous studies to perform the two-class classification task. Previous results achieved high accuracies but low sensitivity, because of the imbalanced feature of the datasets. There are several ways to deal with imbalanced data, but two sampling methods are compared in this study.