Unnatural Feature Engineering: Evolving Augmented Graph Grammars for Argument Diagrams
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Xue:Unnatural:2016,
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author = "Linting Xue and Collin F. Lynch and Min Chi",
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title = "Unnatural Feature Engineering: Evolving Augmented
Graph Grammars for Argument Diagrams",
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booktitle = "Proceedings of the 2016 Conference on Educational Data
Mining, EDM16",
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year = "2016",
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editor = "Tiffany Barnes and Min Chi and Mingyu Feng",
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pages = "255--262",
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address = "Raleigh, USA",
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month = jun # " 29-" # jul # " 2",
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publisher = "International Educational Data Mining Society",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Computation, Augmented Graph Grammars, Argument
Diagramming, Feature Engineering",
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URL = "http://www.educationaldatamining.org/EDM2016/proceedings/paper_137.pdf",
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size = "8 pages",
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abstract = "Graph data such as argument diagrams has become
increasingly common in EDM. Augmented Graph Grammars
are a robust rule formalism for graphs. Prior research
has shown that hand-authored graph grammars can be used
to automatically grade student-produced argument
diagrams. But hand-authored rules can be time consuming
and expensive to produce, and they may not generalize
well to novel contexts. We applied Evolutionary
Computation to automatically induce empirically-valid
graph grammars for argument diagrams that can be used
for automatic grading or provide the basis for hints.
Our results show that our approach can generate more
relevant rules than experts or other state of the art
algorithms, and that these evolved rules outperform the
alternatives.",
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notes = "http://www.educationaldatamining.org/EDM2016/proceedings.html",
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cv-category = "Peer-Reviewed Conference Paper",
- }
Genetic Programming entries for
Linting Xue
Collin Lynch
Min Chi
Citations