Learning classifier systems (LCS)

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Title: Learning classifier systems (LCS)
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My Notes
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April 25, 2020


References
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() https://en.wikipedia.org/wiki/Learning_classifier_system
    Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component
    (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning).
    Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions
    (e.g. behavior modeling, classification, data mining, regression, function approximation, or game strategy).
    This approach allows complex solution spaces to be broken up into smaller, simpler parts.

() search: learning classifier systems

() https://github.com/ryanurbs/eLCS 
    Educational Learning Classifier System (eLCS) - Implementation of a basic, generic Michigan-style LCS algorithm
    Python
    KSW I downloaded the .zip from the website, and ran the following: (it works)
     cd Demo_1
     python ./eLCS_Run.py

() https://github.com/ryanurbs/eLCS_JupyterNotebook
    Educational Learning Classifier System (eLCS) - Jupyter Notebook implementation of a basic, generic Michigan-style LCS algorithm

() https://github.com/ryanurbs/ExSTraCS_2.0
    Extended Supervised Tracking and Classifying System (ExSTraCS) Version 2.0.2 Beta (Python 3.5)

() https://github.com/tofti/java-zcs
    Java Implementation of the simplified strength based learning classifier system 

() https://www.researchgate.net/publication/26850330_Learning_Classifier_Systems_A_Complete_Introduction_Review_and_Roadmap
    Learning Classifier Systems: A Complete Introduction, Review, and Roadmap
    Ryan J. Urbanowicz, Jason H Moore, 2009
    Abstract
    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.

]]></Notes>  

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