Computational Intensity Prediction Model of Vector Data Overlay with Random Forest Method

• 作者: Han Cao Qian Wang Yan-Hui Guo
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: RANDOM FOREST Space analysis VECTOR data COMPUTATIONAL INTENSITY Machine learning
• 年，卷（期）: 2017,（1）
• 页码范围: 147-149
• 页数: 3页
• 分类号: C5

摘要

Spatial analysis is the core of geographic information system(GIS),of which,spatial overlay of vector data is a major job.Computational intensity of the spatial overlay has a direct effect on the overall performance of the GIS.High precision modeling for the computational intensity and analysis of the vector data overlay has been a challenging task.Thus,the paper proposes a novel approach,which utilizes self-learning and self-training features of optimized random forest algorithm to the vector data overlay analysis.Simulation experiments show that the proposed model is superior to non-optimized random forest algorithm and support vector machine model with higher prediction precision and is also capable of eliminate redundant computational intensity features.