New feature for Complex Network based on Ant Colony Optimization for High Level Classification

• Main Author: Chire-Saire, Josimar E
• Format: Journal Article
• Language: English
• Published: 28-08-2020
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Learning; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2008.12884

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.