Induction of Landtype Classification Rules from GIS Data
نویسندگان
چکیده
The feasibility of inducing classification rules for Landtype Associations (LTAs) from instances of human-expert classifications was tested by evaluating the accuracy of 3 rule-induction algorithms on data drawn from a GIS coverage of Southeast Wyoming. In 10-fold cross-validation tests, the accuracy of rules using precipitation, vegetation, geology, elevation, slope, and aspect as features achieved over 87% accuracy. Adding position as a feature increased accuracy to over 95%. Pruning rule sets to increase comprehensibility caused only a slight decrease in accuracy, particularly for the most accurate induction algorithm, RIPPER. The evaluation indicates that human expert LTA classification rules can be effectively induced from examples and applied to large GIS coverages.
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