NiaClass’ documentation!

NiaClass is a framework for solving classification tasks using nature-inspired algorithms. The framework is written fully in Python. Its goal is to find the best possible set of classification rules for the input data using the NiaPy framework, which is a popular Python collection of nature-inspired algorithms. The NiaClass classifier supports numerical and categorical features.

• Free software: MIT license

• Github repository: https://github.com/lukapecnik/NiaClass

• Python versions: 3.7.x, 3.8.x, 3.9.x

The main documentation is organized into a couple of sections:

• User Documentation

• Developer Documentation

• About

User Documentation

• Getting Started
  • Examples
  • Optimization Algorithms

Developer Documentation

• Installation
  • Setup development environment
• Testing
• Documentation
• API
  • niaclass

About

• Contributing to NiaClass
  • Code of Conduct
  • How Can I Contribute?
    • Reporting Bugs
    • Suggesting Enhancements
    • Pull requests
• Contributor Covenant Code of Conduct
  • Our Pledge
  • Our Standards
  • Enforcement Responsibilities
  • Scope
  • Enforcement
  • Enforcement Guidelines
    • 1. Correction
    • 2. Warning
    • 3. Temporary Ban
    • 4. Permanent Ban
  • Attribution

References

[1] Iztok Fister Jr., Iztok Fister, Dušan Fister, Grega Vrbančič, Vili Podgorelec. [On the potential of the nature-inspired algorithms for pure binary classification](http://www.iztok-jr-fister.eu/static/publications/267.pdf). In. Computational science - ICCS 2020 : 20th International Conference, Proceedings. Part V. Cham: Springer, pp. 18-28. Lecture notes in computer science, 12141, 2020