DownloadFuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC or SQLite; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.
Features
- Based on the intuitionistic fuzzy sets and the possibility theory
- All the data the system operates on are considered fuzzy
- Fuzzy classes, within the framework classes take a natural interpretation of distinguished features
- Numeric, enumeration features and features based on linguistic variables
- Open for definition of new features beyond built-in classes of numeric, nominal and linguistic ones
- Derived and evaluated features. Along with the measured features the system supports the features deduced from other features
- Classifiers as features for building hierarchical systems
- Automatic refinement in case of dependent features
- Incremental learning support
- Extended fuzzy control language support
- Object-oriented software design
- Features, training sets and classifiers are extensible objects
- Automatic garbage collection
- Generic data base support through ODBC and SQLite
- Ada 95, 2005, 2012 compliant. GTK+ GUI requires at least Ada 2005
- Text I/O is provided for teaching sets and classifiers. Teaching sets can be imported in an intuitive format from text files
- Training sets and classifiers can be output in directly HTML format, supporting a web-ready solution
- GTK+ 3 GUI. The GUI is optional the system can be used fully programmatically
- Delivered with an set of samples varying from ones illustrating usage of the system components to examples of training on real- life and size data
Project Samples
