论文标题
基于蚂蚁菌落优化的高级分类的复杂网络的新功能
New feature for Complex Network based on Ant Colony Optimization for High Level Classification
论文作者
论文摘要
低级分类从元素中提取特征，即物理用来训练模型以进行以后的分类。高级分类使用高级特征，存在的模式，数据之间的关系，并结合了分类的低水平和高级特征。可以从数据创建的复杂网络中获得高级功能。局部和全局特征用于描述复杂网络的结构，即平均邻居度，平均聚类。目前的工作提出了一个新颖的功能，以描述蚂蚁菌落系统方法的网络架构。实验显示了使用此功能的优点，因为具有不同类别数据的感性。
Low level classification extracts features from the elements, i.e. physical to use them to train a model for a later classification. High level classification uses high level features, the existent patterns, relationship between the data and combines low and high level features for classification. High Level features can be got from Complex Network created over the data. Local and global features are used to describe the structure of a Complex Network, i.e. Average Neighbor Degree, Average Clustering. The present work proposed a novel feature to describe the architecture of the Network following a Ant Colony System approach. The experiments shows the advantage of using this feature because the sensibility with data of different classes.
