Tools, algorithms and data structures for rough sets and explainable machine learning, including algorithms for discernibility matrix, reducts, decision rules, classification (KNN, NeuralNet, AQ15, RoughSet, RIONIDA, SVM, C4.5, and many others), discretization (1R, EntropyMinimization, ChiMerge, MD), and tool for explainable and interactive ML.

Rough Set Python Package is a Python library that provides a set of tools to calculate rough sets and obtain reduct rules.

An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection

A small demo Python implementation of some (fuzzy) roughset algorithms such as LEM2

NRough - Rough Sets and Machine Learning Framework in .NET

Meta learning framework based on rough set measures

Implementation of Rough Computing concepts

Implementation of LEM2 inference rule algorithm created with Python.

Framework for developing fuzzy and fuzzy-rough classification models for multiple instance data

Rough set class library for machine learning

An accountant for transactional data

Artificial data set generator for machine learning purpose

Implementation of Hybrid fuzzy-rough Rule induction and feature selection paper 2009 by Richard Jensen

Distributed Rough Set Theory for Feature Selection

Implementation of an ML RS1 algorithm based on the theory of rough sets by Zdzislaw Pawlak

Implementation of "Applying FOSS Support Vector Machine and Rough Sets on COVID-19 Cases Triage"

The need for missing value imputation is of extreme importance in big data applications as data volumes tend to grow exponentially and their data structures change rapidly.

NRough - Rough Sets and Machine Learning Framework in .NET

A skin cancer diagnosis system using CNN and LSTM analyzes sequential images of skin lesions. This combines image analysis with temporal data, aiding in early detection and monitoring of skin conditions.