计算机应用研究2012，Vol.29Issue(3)：1146-1148,1151,4.DOI:10.3969/j.issn.1001-3695.2012.03.095
一种改进的SVM决策树及在遥感分类中的应用
Improved SVM decision-tree and its application in remote sensing classification
摘要
Abstract
This paper presented a SVM decision-tree algorithm based on GA and KNN. First, GA was used to create optimal or near-optimal decision-tree, which defined a novel separability measure. Then in the class phase, standard SVM was used to make binary classification for the divisible nodes, and SVM combined with KNN were used to classify the fallible nodes. Finally, achieved the multi-classification by the SVM decision-tree. Experimental results show that the proposed method can effectively improve the classification precision of remote sensing image in comparison to traditional classification methods.关键词
遗传算法/K近邻/支持向量机决策树/遥感图像分类Key words
genetic algorithm/ K-nearest neighbors/ support vector machine ( SVM ) decision-tree/ classification of remote sensing image分类
信息技术与安全科学引用本文复制引用
丁胜锋,孙劲光,陈东莉,李扬,姜晓林..一种改进的SVM决策树及在遥感分类中的应用[J].计算机应用研究,2012,29(3):1146-1148,1151,4.基金项目
辽宁省科技计划资助项目(2010401010) （2010401010）
