Learning effective classifiers with Z-value measure based on genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Chien:2004:PR,
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author = "Been-Chian Chien and Jung-Yi Lin and Wei-Pang Yang",
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title = "Learning effective classifiers with Z-value measure
based on genetic programming",
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journal = "Pattern Recognition",
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year = "2004",
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volume = "37",
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pages = "1957--1972",
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number = "10",
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abstract = "This paper presents a learning scheme for data
classification based on genetic programming. The
proposed learning approach consists of an adaptive
incremental learning strategy and distance-based
fitness functions for generating the discriminant
functions using genetic programming. To classify data
using the discriminant functions effectively, the
mechanism called Z-value measure is developed. Based on
the Z-value measure, we give two classification
algorithms to resolve ambiguity among the discriminant
functions. The experiments show that the proposed
approach has less training time than previous GP
learning methods. The learned classifiers also have
high accuracy of classification in comparison with the
previous classifiers.",
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owner = "wlangdon",
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URL = "http://www.sciencedirect.com/science/article/B6V14-4CPVJFT-3/2/51f0ecbd7d198da15f4ae094e378c5d0",
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month = oct,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1016/j.patcog.2004.03.016",
- }
Genetic Programming entries for
Been-Chian Chien
Mick Jung-Yi Lin
Wei-Pang Yang
Citations