Developing an effective validation strategy for genetic programming models based on multiple datasets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{conf/iri/LiuKY06,
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title = "Developing an effective validation strategy for
genetic programming models based on multiple datasets",
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author = "Yi Liu and Taghi M. Khoshgoftaar and Jenq-Foung Yao",
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year = "2006",
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booktitle = "2006 IEEE International Conference on Information
Reuse and Integration",
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pages = "232--237",
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address = "Waikoloa Village, HI, USA",
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month = sep,
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publisher = "IEEE",
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bibdate = "2006-11-14",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iri/iri2006.html#LiuKY06",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/IRI.2006.252418",
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abstract = "Genetic programming (GP) is a parallel searching
technique where many solutions can be obtained
simultaneously in the searching process. However, when
applied to real-world classification tasks, some of the
obtained solutions may have poor predictive
performances. One of the reasons is that these
solutions only match the shape of the training dataset,
failing to learn and generalise the patterns hidden in
the dataset. Therefore, unexpected poor results are
obtained when the solutions are applied to the test
dataset. This paper addresses how to remove the
solutions which will have unacceptable performances on
the test dataset. The proposed method in this paper
applies a multi-dataset validation phase as a filter in
GP-based classification tasks. By comparing our
proposed method with a standard GP classifier based on
the datasets from seven different NASA software
projects, we demonstrate that the multi-dataset
validation is effective, and can significantly improve
the performance of GP-based software quality
classification models",
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notes = "http://ieeexplore.ieee.org/servlet/opac?punumber=4018442",
- }
Genetic Programming entries for
Yi Liu
Taghi M Khoshgoftaar
Jenq-Foung Yao
Citations