摘要
昆虫自动识别是重要的新兴研究领域,其中特征筛选与恰当地将多分类转化为二分类是两个关键步骤。本文基于支持向量分类,提出了一种新的多类昆虫自动鉴别方法:先以初始样本互作转换将多分类转化为二分类,再以可交换核函数消除互作样本中初始样本排列顺序不同的影响,继以非线性筛选去除无关特征与冗余特征并给出各保留特征相对重要性排序,最后以简单投票决策校正独立预测结果。新方法应用于2科7种蝶类自动鉴别,以前翅9个翅脉交叉点距离为初始特征,种、科阶元26、24个随机初始测试样本均获得了100%的准确鉴别。新方法在昆虫自动识别等多分类领域有广泛应用前景。
Automatic identification of insects is an important and emerging area of research.The screening features and transforming multi-class classification into two-class classification properly are two key procedures in the process.In this article,a novel method for automatic identification of multi-class insects was developed based on support vector classification（SVC）.Firstly,the initial multi-class samples were transformed into two-class samples with interaction transformation.Secondly,a symmetrical kernel function was inducted to solve the rank problem of the two initial samples in interaction sampling pair.Thirdly,irrelevant and redundant features were eliminated nonlinearly with SVC and the relative importances of kept features were listed.Lastly,the prediction results were further corrected by simple-vote decision.The new method was applied to identify the butterflies of seven species at species level and family level,and the accuracies at both levels are 100%.The results show that the new method can be widely used in the prediction area of multi-class classification,such as automatic identification of insects.
出处
《昆虫学报》
CAS
CSCD
北大核心
2011年第5期609-614,共6页
Acta Entomologica Sinica
基金
湖南省杰出青年基金(10JJ1005)
湖南省教育厅青年基金(05B025)
湖南省2008年高校科技创新团队项目
关键词
支持向量分类
蝴蝶
自动识别
特征筛选
互作转换
Support vector classification（SVC）
butterfly
automatic identification
screening features
interaction transformation
