Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach

Holmes, John H.; Sager, Jennifer A.

doi:10.1007/11527770_60

Cited by 24 publications

(17 citation statements)

References 6 publications

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“…Holmes with colleagues successfully applied his version of NEWBOOLE, called EpiCS, to the analysis epidemiologic surveillance data where adaptivity to abrupt changes is mandatory [135][136][137][138]140]. Later, he extended the results to XCS by applying his new model, EpiXCS, to the same type of data [139,141].…”

Section: Learning Classifier Systems For Classification and Data Miningmentioning

confidence: 97%

Learning classifier systems: then and now

Lanzi

2008

Evol. Intel.

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Section: Learning Classifier Systems For Classification and Data Miningmentioning

confidence: 97%

Learning classifier systems: then and now

Lanzi

2008

Evol. Intel.

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“…The significance of multiagent systems for AIME was underlined by a keynote lecture on this theme in 2001 [101]. Another research stream related to this theme uses evolutionary approaches such as genetic algorithms to solve search problems [102,103].…”

Section: Distributed and Cooperative Systemsmentioning

confidence: 99%

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Peek

Combi

Marín

et al. 2015

Artificial Intelligence in Medicine

100

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“…One of the earliest attempts to apply an LCS algorithm to such a problem was [28]. Soon after, John Holmes initiated a lineage of LCS designed for epidemiological surveillance and knowledge discovery which included BOOLE++ [81], EpiCS [82], and most recently EpiXCS [143]. Similar applications include [93,95,130,142,[185][186][187], all of which examined the Wisconsin breast cancer data taken from the UCI repository [188].…”

Section: Biological Applicationsmentioning

confidence: 99%

Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Urbanowicz

Moore

2009

Journal of Artificial Evolution and Applications

222

131

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If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.

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Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach

Cited by 24 publications

References 6 publications

Learning classifier systems: then and now

Learning classifier systems: then and now

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

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