Comments on: Python LCS Implementations (XCS, UCS, MCS) for SNP Environment http://gbml.org/2010/03/24/python-lcs-implementations-xcs-ucs-mcs-for-snp-environment/ The LCS and GBML community stop Thu, 17 Feb 2011 04:58:24 +0000 hourly 1 http://wordpress.org/?v=3.3.1 By: Ryan J. Urbanowicz http://gbml.org/2010/03/24/python-lcs-implementations-xcs-ucs-mcs-for-snp-environment/#comment-26 Ryan J. Urbanowicz Fri, 16 Jul 2010 19:31:57 +0000 http://lcs-gbml.ncsa.uiuc.edu/?p=3667#comment-26 The sample datasets included for *_Test.py were designed to model only partially penetrant epistatic, and heterogeneous effects. So even if the a given LCS was perfectly optimized and run for an infinate amount of time, it could not reach a testing accuracy of 100%. I'm not sure off hand what the max is for these datasets, but I'd estimate somewhere between 60-75% max testing accuracy. Each of these implementations were written specific to our problem domain, and will likely require modifications to address others. Hope that helps! The sample datasets included for *_Test.py were designed to model only partially penetrant epistatic, and heterogeneous effects. So even if the a given LCS was perfectly optimized and run for an infinate amount of time, it could not reach a testing accuracy of 100%. I’m not sure off hand what the max is for these datasets, but I’d estimate somewhere between 60-75% max testing accuracy. Each of these implementations were written specific to our problem domain, and will likely require modifications to address others. Hope that helps!

]]> By: Chris http://gbml.org/2010/03/24/python-lcs-implementations-xcs-ucs-mcs-for-snp-environment/#comment-25 Chris Fri, 16 Jul 2010 17:44:43 +0000 http://lcs-gbml.ncsa.uiuc.edu/?p=3667#comment-25 I ran the *_Test.py files for the XCS, UCS, and MCS algorithms. They all appear to do little better at classifying a binary class than a random flip of a coin. Am I reading these results right? e.g. for UCS, it says "89 out of 160 correctly classified." for a 55% accuracy? I ran the *_Test.py files for the XCS, UCS, and MCS algorithms. They all appear to do little better at classifying a binary class than a random flip of a coin. Am I reading these results right?

e.g. for UCS, it says “89 out of 160 correctly classified.” for a 55% accuracy?

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