基于数据挖掘分类法的农用地分等

应用决策树模型、BP神经网络和Logistic回归模型等分类法，对龙川县农用地分等进行了实证研究，并对各方法的分等结果有效性进行了评价，同时利用混淆矩阵探讨了样本数量对3种模型分类精度的影响。结果表明，样本数量对模型影响有差异，其中对BP神经网络和决策树模型影响较大，在较多训练样本时，模型的精度较高。在较多样本支持下，BP神经网络精度最高，但训练模型的时间较长，可解释性差；决策树模型既具有较高的精度又具有良好的可解释性；Logistic回归模型表现较差。决策树模型最适合龙川县农用地分等工作。研究结果表明，数据挖掘分类法是有效而准确的土地评价方法，有助于提高土地评价的精度和准确性，对农用地分等方法的优化具有一定的借鉴意义。

Farmland classification based on data mining classification method

Decision tree, BP neural network, and logistic model were used to explored farmland classification of Longchuan Country. The effectiveness of results was analyzed. Confusion matrix was adapted to probe into accuracy of the classification. The results showed that the influences of the number of samples were different to three models. With more training samples, BP neural network and decision tree had heavier influence and higher accuracy in comparison with logistic model. Besides of three models, BP neural network had the highest accuracy and needed a longer time to train model with poor interpretation; decision tree had higher accurate and good interpretation; Logistic model performed worst, Therefore, decision tree might be the best model for farmland classification in Longchuan Country. So data mining classification is an effective and exact method for farmland evaluation, which will enhance the precision and accuracy of land evaluation, and is of significance for the optimization of farmland classification method.