Description

Rseslib 3 is a library of rough set, machine learning and data mining data structures, algorithms and tools implemented in Java. It was started as the open source successor of RSES 2 by Group of Logic at Faculty of Mathematics, Informatics and Mechanics, University of Warsaw.

Rseslib 3 provides:

  • 70 various open source algorithms from rough set theory, machine learning and data mining in Java, including 13 classification models
  • modular component-based architecture where source code components can bee easily reused, substituted and shared among algorithms
  • QMAK platform for visualization of classification models and classification processes, also enabling interactive experimentation and tweaking of the trained models (watch 5-minute demo)
  • Simple Grid Manager, port to Weka and command-line programs

30+ people have already contributed to Rseslib source code and it is still developing. Rseslib classifiers have been proven by independent researchers to rank among the classifiers with the highest classification accuracy (see Rseslib achievements).

Tools

QMAK

GUI tool for explainable machine learning allowing users to interact with the trained models and visualizing classification process.

Weka

Rseslib 3 is available in Weka as official Weka package. The instruction on how to install Rseslib in Weka can be found in Chapter 14 WEKA of Rseslib User Guide.

Simple Grid Manager

Client-server tool for running Rseslib experiments on many computers or cores. Tutorial on how to run computations in cluster can be found in Chapter 16 SGM: Computing many experiments on many computers/cores of Rseslib User Guide.

Projects using Rseslib 3

  • TunedIT - system for automated evaluation, benchmarking and comparison of data mining and machine learning algorithms
  • Debellor - framework for scalable data mining and machine learning with data streaming
  • mahout-extensions - attribute selection extensions to Mahout, an extensible programming environment and framework for building scalable algorithms in machine learning
  • DMEXL - data mining expression library facilitating development of concurrent data mining algorithms