Generating Effective Classifiers with Supervised Learning of Genetic Programming
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gp-bibliography.bib Revision:1.8178
- @InProceedings{Chien:2003:DaWaK,
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author = "Been-Chian Chien and Jui-Hsiang Yang and
Wen-Yang Lin",
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title = "Generating Effective Classifiers with Supervised
Learning of Genetic Programming",
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booktitle = "Data Warehousing and Knowledge Discovery: 5th
International Conference, DaWaK 2003",
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year = "2003",
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volume = "2737",
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series = "Lecture Notes in Computer Science",
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pages = "192--201",
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address = "Prague, Czech Republic",
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month = "3-5 " # sep,
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1007/b11825",
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ISBN = "3-540-40807-X",
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abstract = "A new approach of learning classifiers using genetic
programming has been developed recently. Most of the
previous researches generate classification rules to
classify data. However, the generation of rules is time
consuming and the recognition accuracy is limited. In
this paper, an approach of learning classification
functions by genetic programming is proposed for
classification. Since a classification function deals
with numerical attributes only, the proposed scheme
first transforms the nominal data into numerical values
by rough membership functions. Then, the learning
technique of genetic programming is used to generate
classification functions. For the purpose of improving
the accuracy of classification, we proposed an adaptive
interval fitness function. Combining the learned
classification functions with training samples, an
effective classification method is presented. Numbers
of data sets selected from UCI Machine Learning
repository are used to show the effectiveness of the
proposed method and compare with other classifiers.",
- }
Genetic Programming entries for
Been-Chian Chien
Jui-Hsiang Yang
Wen-Yang Lin
Citations