New feature for Complex Network based on Ant Colony Optimization for High Level Classification
This work addresses classification tasks by enhancing feature extraction from complex networks, but it appears incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of high-level classification by proposing a novel feature derived from Ant Colony Optimization to describe complex network architecture, showing improved sensitivity to different data classes in experiments.
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.