Abstract

The data employed in urban gain modeling classes are often imbalanced, negatively affecting the accuracy of traditional and standard data mining and machine learning models. This study presents a new framework on the basis of clustering-based modeling and ensemble models to deal with the class imbalance problem in urban gain modeling. The random forest (RF), artificial neural network (ANN) and support vector machine (SVM) models served as the base models for the generation and evaluation of the results within this framework. The changes in urban land-use pattern of Isfahan in Iran in two time intervals of 1994-2004 and 2004-2014 were considered for the modeling. The findings showed that the proposed sampling strategy yields higher Hits and Correct Rejections rates than the strategies applied in previous studies in all three models. In the second part of the proposed framework (ensemble models), there was no substantial difference in the confusion matrix entries.

摘要

城市增益建模类中使用的数据通常不平衡，从而对传统和标准数据挖掘及机器学习模型的准确性产生负面影响。这项研究提出了一个基于聚类的模型和集成模型的新框架，以解决城市收益建模中的类不平衡问题。随机森林（RF），人工神经网络（ANN）和支持向量机（SVM）模型是在此框架内生成和评估结果的基础模型。该模型考虑了伊朗伊斯法罕的城市土地利用格局在1994-2004年和2004-2014年两个时间间隔内的变化。研究结果表明，与所有三个模型中以前的研究中采用的策略相比，拟议的采样策略产生更高的命中率和正确拒绝率。