YALE
From Wikipedia, the free encyclopedia
- For other uses of Yale, see Yale (disambiguation).
YALE ("Yet Another Learning Environment") is an environment for machine learning and data mining experiments. It allows experiments to be made up of a large number of arbitrarily nestable operators and they are described in XML files which can easily be created with YALE's graphical user interface. Applications of YALE cover both research and real-world data mining tasks.
It was developed by the Artificial Intelligence Unit of the Dortmund University since 2001. It has a GNU license, and is hosted by SourceForge since 2004.
YALE provides more than 350 operators for all main machine learning procedures, including input and output, and data preprocessing and visualization. It is written in the Java programming language and therefore can work on all popular operating systems. It also integrates all learning schemes and attribute evaluators of the Weka learning environment.
[edit] Properties
Some properties of YALE are:
- written in Java
- knowledge discovery processes are modeled as operator trees
- internal XML representation ensures standardized interchange format of data mining experiments
- scripting language allows for automatic large-scale experiments
- multi-layered data view concept ensures efficient and transparent data handling
- graphical user interface, command line mode (batch mode), and Java API for using YALE from your own programs
- plugin and extension mechanisms, several plugins already exist
- plotting facility offering a large set of high-dimensional visualization schemes for data and models
- applications include text mining, multimedia mining, feature engineering, data stream mining and tracking drifting concepts, development of ensemble methods, and distributed data mining.
[edit] References
- Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
