Post date: Jun 24, 2014 2:42:59 PM
I'm pleased to report that the first version of ExSTraCS is now available for download on Sourceforge. This code pairs with our first publication on ExSTraCS that was accepted at Parallel Problem Solving in Nature (PPSN) 2014. ExSTraCS is a Michigan-Style Learning Classifier System (or more generally, a stochastic machine learning algorithm) designed specifically for data mining, classification, prediction, and knowledge discovery tasks in noisy, complex, supervised learning problems. ExSTraCS v1.0 is flexible, able to handle discrete or continuous attributes in the dataset, and binary or multiclass endpoints. ExSTraCS integrates a number of recent successful LCS algorithmic components into a single, platform for application and further development. Specifically ExSTraCS combines, expert knowledge covering, attribute tracking, attribute feedback, rule compaction strategies, and a flexible knowledge representation scheme, along with a number of other algorithmic improvements designed to make ExSTraCS more flexible, user friendly, and powerful.