Rough set and machine learning data structures, algorithms and tools, including algorithms for discernibility matrix, reducts, decision rules, classification (RoughSet, KNN, RIONIDA, AQ15, C4.5, SVM, NeuralNetwork and many others), discretization (1R, Entropy Minimization, ChiMerge, MD), and tool for interactive and explainable machine learning.

LEM2 rule induction algorithm.

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

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.

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

Work on combining Logit model with an information granulation method for better interpretability

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

Floats may be unmarked, marked or remarked without slowing computations.

Implementation of Rough Computing concepts

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

An accountant for transactional data

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

Meta learning framework based on rough set measures

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

Rough set class library for machine learning

Artificial data set generator for machine learning purpose

Distributed Rough Set Theory for Feature Selection

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

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.