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Proceedings Paper

The comparative analysis of various classification models on land evaluation
Author(s): Jian Tian; Yueming Hu; Jianmin Liu; Yanling Zhao; Changwei Wang
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Paper Abstract

Many methods of data mining model were widely applied for land evaluation, and they show different characteristics of the application for land evaluation. In order to analyze different classification model effect for land evaluation, this paper took land in Longchuan County as a case study, established three models using decision tree, back propagation neural network (BP) and logistic regression on land evaluation. The result of study shows that the accuracy of three models changes remarkably according to 6 groups of training samples. The accuracy of the decision tree and BP model can reach high level in support of 4000 training samples, but decision tree model is superior to BP model at intelligibility of model and consuming-time aspects. The overall performance of Logistic regression model is worse than other models at the massive samples. Moreover, three model have different the characteristic of error distribution by means of confusion matrix. The error of decision tree distributes evenly, and the error distribution of BP has opposite result of Logistic regression. Results indicate that the model of decision tree is the best model for evaluating Longchun County land at comprehensive thought, and it has a good effect on application.

Paper Details

Date Published: 14 October 2009
PDF: 9 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921A (14 October 2009); doi: 10.1117/12.837587
Show Author Affiliations
Jian Tian, Hefei Univ. of Technology (China)
Yueming Hu, South China Agricultural Univ. (China)
Jianmin Liu, Hefei Univ. of Technology (China)
Yanling Zhao, Hefei Univ. of Technology (China)
Changwei Wang, South China Agricultural 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)

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