Rule-based machine learning classification and knowledge discovery for complex problems
Ryan J. Urbanowicz · ACM SIGEVOlution · 2015
Learning classifier systems (LCSs) are an advantageous, powerful, and flexible class of algorithms that have, to date, been underutilized largely due to the perception that they are difficult to apply, evaluate, and interpret. ExSTraCS is an Ex tended S upervised Tra cking and C lassifying S ystem based on the Michigan-Style LCS architecture [4]. It offers an accessible, user friendly LCS platform for supervised rule-based machine learning, classification, data mining, prediction, and knowledge discovery. ExSTraCS seeks to make no assumptions about the data, and is therefore model free and particularly well suited to complex problems that are multi-factorial, interacting (non-linear), heterogeneous, noisy, class imbalanced, multi-class, or larger-scale. ExSTraCS is written in Python, open source, well documented, and freely available at sourceforge.net.