Application of binary tree based SVMs approach to land grade evaluation

Abstract

Support vector machines (SVMs), which are based on statistical learning theory, have recently got considerable potential in data mining by their good ability of generalization. Land grade evaluation, which provides information for land planning and decision-making, is a process of evaluating land quality for a particular use. It can be referred to as multiclass classification problem. As a result of SVMs' binary nature, they can not be directly applied to land grading process. By integrating SVMs with a binary tree, this paper applied a binary tree based SVMs (BTSVMs) approach into land grade evaluation. Arable land in Heping County, Guangdong, was chosen as study area and BTSVMs model was then applied to the data. In addition to BTSVMs, the same data were classified using decision tree (DT) and artificial neural network (ANN). Compared with DT and ANN, results showed that BTSVMs had better classification accuracy. While decreasing the size of training data, the accuracy of each approach dropped down positively with BTSVMs relatively more accurate than others. In general, BTSVMs is potentially feasible in the application of land grade evaluation with its good performance.