Strong Typing, Swarm Enhancement, and Deep Learning Feature Selection in the Pursuit of Symbolic Regression-Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{korns:2018:GPTP,
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author = "Michael F. Korns and Tim May",
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title = "Strong Typing, Swarm Enhancement, and Deep Learning
Feature Selection in the Pursuit of Symbolic
{Regression-Classification}",
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booktitle = "Genetic Programming Theory and Practice XVI",
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year = "2018",
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editor = "Wolfgang Banzhaf and Lee Spector and Leigh Sheneman",
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pages = "58--84",
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address = "Ann Arbor, USA",
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month = "17-20 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-04734-4",
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URL = "http://link.springer.com/chapter/10.1007/978-3-030-04735-1_4",
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DOI = "doi:10.1007/978-3-030-04735-1_4",
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abstract = "Symbolic Classification (SC), an offshoot of Genetic
Programming (GP), can play an important role in any
well rounded predictive analytics tool kit, especially
because of its so called WhiteBox properties. Recently,
algorithms were developed to push SC to the level of
basic classification accuracy competitive with existing
commercially available classification tools, including
the introduction of GP assisted Linear Discriminant
Analysis (LDA). In this paper we add a number of
important enhancements to our basic SC system and
demonstrate their accuracy improvements on a set of
theoretical problems and on a banking industry problem.
We enhance GP assisted linear discriminant analysis
with a modified version of Platt Sequential Minimal
Optimization algorithm which we call (MSMO), and with
swarm optimization techniques. We add a user-defined
typing system, and we add deep learning feature
selection to our basic SC system. This extended
algorithm (LDA++) is highly competitive with the best
commercially available M-Class classification
techniques on both a set of theoretical problems and on
a real world banking industry problem. This new LDA++
algorithm moves genetic programming classification
solidly into the top rank of commercially available
classification tools.",
- }
Genetic Programming entries for
Michael Korns
Tim May
Citations