Share Email Print

Proceedings Paper

Application of binary tree based SVMs approach to land grade evaluation
Author(s): Yin Xia; Yaolin Liu; Xiaofeng Hong; Dianfeng Liu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 14 October 2009
PDF: 7 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921D (14 October 2009); doi: 10.1117/12.838414
Show Author Affiliations
Yin Xia, Wuhan Univ. (China)
Yaolin Liu, Wuhan Univ. (China)
Xiaofeng Hong, Wuhan Univ. (China)
Dianfeng Liu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

© SPIE. Terms of Use
Back to Top