基于集成学习和元胞自动机的城市地理模拟

集成学习通过将若干弱分类器集成以取得比单个弱分类器更好的性能，是机器学习的重要研究方向。针对常用城市地理模拟系统中元胞自动机转换规则获取算法的局限性，本文提出基于集成学习算法的元胞自动机，并将其应用于城市建设用地的动态模拟。以决策树作为弱分类器，应用集成学习算法和元胞自动机，对东莞市2001年到2005年的建设用地时空格局进行了模拟，取得了较好的模拟效果。精度评估结果表明，经集成学习后的决策树比单个决策树对城市建设用地动态的模拟精度更高，算法泛化能力更好。

Ensemble-learning-based Cellular Automata for Urban Geosimulation

This paper presents a new method to simulate urban dynamics based on ensemble learning, cellular automata and GIS. Ensemble learning is an important direction of the machine learning which can improve the performance than the single classifier by constructing a set of classifier and then classify the new data set by taking a weighted vote of their predictions. Many algorithms have been used to simulate the change of land use so far. However, there exists some limitations about the algorithms in the applications . For instance, the dicision trees algorithm is always confronting the problem of over-fitting, the neural network algorithm would be trapped in local optimum easily. This paper uses the dicision tree algorithm as the classifier of the ensemble learning to predict urban dynamics of Dongguan city. The result shows that the simulation of the ensemble learning is better than the decision tree.