基于集成特征选择的森林火灾风险评估

摘 要：采用集成学习的思想，提出了一种基于集成特征选择的森林火灾风险评估方法。以特征选择方法的多样性和独立性为考量，选择了15种特征选择器并利用差异度进行筛选，获得异质选择器集合，进而得到特征子集集合。其次，利用各特征子集分别构建基于BP神经网络的森林火灾风险评估模型，并依据模型准确度筛选林火重要影响因子，构建最优森林火灾风险评估模型。结果表明，该算法准确度为85.96%，具有良好的泛化能力，可实现对森林火灾风险的有效评估。

Forest fire risk assessment based on ensemble feature selection

This paper proposes a forest fire risk assessment method based on ensemble feature selection. Considering the diversity and independence of algorithms, 15 kinds of feature select algorithms are selected to form the heterogeneous selectors based on their difference. By using the feature select algorithms, a feature subset set is obtained. And then a forest fire risk assessment model is constructed based on BP neural network by using each feature subset. The important factors of forest fires are selected based on the accuracy of neural network to construct the optimal forest fire risk assessment model. The results show that the accuracy of the algorithm proposed is 85.96%. The proposed model has good generalization ability and can assess forest fire risk effectively.