Description
Started as the open source successor of RSES 2 (rough set based system for data analysis) by Group of Logic at Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Rseslib 3 has evolved through the universal machine learning and data mining library providing 70 various algorithms including 13 classification models, to an integrated platform for explainable visualized machine learning.
QMAK, a GUI tool included in Rseslib 3, is a platform for visualization of classification models and classification processes, also enabling interactive experimentation and tweaking of the trained models. It enables to understand why a particular decision was selected and interpret it correctly, and to improve the trained models interactively. This one visualization tool aggregates multiple models and allows the community to add more visualized models through a simple connecting interface. Watch 5-minute demo to see how QMAK works.
Rseslib 3 has not lost its previous objectives being a great source of rough set, machine learning and data mining tools, algorithms and data structures in Java that can be used in Weka, Simple Grid Manager and command-line programs or as a software library. Integrating the work of 30+ contributors, a huge amount of effort has gone into modular component-based architecture of the library so that its components can be easily reused, substituted and shared among algorithms.
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