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

TL;DR

This paper introduces a novel complex network feature based on Ant Colony Optimization to improve high level classification by capturing network architecture, demonstrating increased sensitivity to data class differences.

Contribution

The work proposes a new network feature derived from Ant Colony System to enhance high level classification in complex networks.

Findings

The new feature improves classification sensitivity.

Experiments show better performance with the proposed feature.

The approach effectively captures network architecture differences.

Abstract

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.