摘要
针对传统主动学习单一策略算法在挑选最有价值未标记样本时出现的抖动和不稳定的现象,引入集成学习(ensemble learning)分类器的加权组合思想,提出一种基于组合策略的联合挑选(ESAL)方法,将模型的组合衍生至策略的组合,从而实现单一模型多策略的融合,获得更高的稳定性。通过对高光谱遥感图像分类结果的分析可以看出,在获得相同精度阈值时,ESAL算法相对于单一策略算法最高可节省成本25.4%,抖动频率减少至原来的16.67%,抖动明显改善,体现出ESAL算法良好的稳定性。
In order to improve the phenomena of jitter and instability of the traditional active learning single strategy algorithm in selecting the most valuable unlabeled samples.The idea of weighted combination of ensemble learning classifier and proposes a joint selection based on the combination strategy method(ESAL,ensemble strategy active learning)was introduced,the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability.By analyzing the classification results of hyperspectral remote sensing images,the ESAL algorithm can save 25.4%of the cost compared with the single strategy algorithm and reduce the jitter frequency to 16.67%when the same accuracy threshold is obtained,and the jitter is obviously improved.ESAL algorithm is out of good stability.
作者
崔颖
徐凯
陆忠军
刘述彬
王立国
CUI Ying;XU Kai;LU Zhongjun;LIU Shubin;WANG Liguo(College of Information and Communications Engineering,Harbin Engineering University,Harbin 150001,China;Remote Sensing Technology Center of Heilongjiang Academy of Agricultural Sciences,Harbin 150086,China)
出处
《通信学报》
EI
CSCD
北大核心
2018年第4期91-99,共9页
Journal on Communications
基金
国家自然科学基金资助项目(No.61675051)
教育部博士点基金资助项目(No.20132304110007)~~
关键词
主动学习
集成学习
高光谱图像
策略组合
active learning
ensemble learning
hyperspectral image
strategy combination
