Swarm Intelligence in Semi-supervised Classification

TL;DR

This paper reviews how swarm intelligence algorithms are applied in semi-supervised classification, highlighting their roles in algorithm tuning, hybrid models, and as core components in machine learning.

Contribution

It provides a comprehensive literature review of swarm intelligence applications specifically in semi-supervised learning, an area with diverse algorithmic uses.

Findings

Swarm algorithms are used for parameter tuning in ML.

Hybrid swarm-ML models are common in semi-supervised learning.

Swarm intelligence serves as a backbone in some ML approaches.

Abstract

This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-supervised classification. There are many research papers for applying swarm intelligence algorithms in the area of machine learning. Some algorithms of SI are applied in the area of ML either solely or hybrid with other ML algorithms. SI algorithms are also used for tuning parameters of ML algorithm, or as a backbone for ML algorithms. This paper introduces a brief literature review for applying swarm intelligence algorithms in the field of semi-supervised learning