ExSTraCS 2.0: description and evaluation of a scalable learning classifier system

Abstract

Algorithmic scalability is a major concern for any machine learning strategy in this age of 'big data'. A large number of potentially predictive attributes is emblematic of problems in bioinformatics, genetic epidemiology, and many other fields. Previously, ExS-TraCS was introduced as an extended Michigan-style supervised learning classifier system that… (More)
DOI: 10.1007/s12065-015-0128-8

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