Title :
CN2-R: Faster CN2 with randomly generated complexes
Author_Institution :
Fac. of Comput., Univ. of Latvia, Riga, Latvia
Abstract :
Among the rule induction algorithms, the classic CN2 is still one of the most popular ones; a great amount of enhancements and improvements to it is to witness this. Despite the growing computing capacities since the algorithm was proposed, one of the main issues is resource demand. The proposed modification, CN2-R, substitutes the star concept of the original algorithm with a technique of randomly generated complexes in order to substantially improve on running times without significant loss in accuracy.
Keywords :
knowledge based systems; learning (artificial intelligence); CN2-R; faster CN2; randomly generated complex; resource demand; rule induction algorithm; Accuracy; Algorithm design and analysis; Classification algorithms; Complexity theory; Iris; Machine learning; Machine learning algorithms;
Conference_Titel :
Methods and Models in Automation and Robotics (MMAR), 2011 16th International Conference on
Conference_Location :
Miedzyzdroje
Print_ISBN :
978-1-4577-0912-8
DOI :
10.1109/MMAR.2011.6031363
