Enhancing k-Nearest Neighbors through Learning Transformation Functions by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Huang:2019:CEC,
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author = "Kuan-Chun Huang and Yu-Wei Wen and Chuan-Kang Ting",
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title = "Enhancing k-Nearest Neighbors through Learning
Transformation Functions by Genetic Programming",
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booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2019",
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pages = "1891--1897",
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abstract = "The k-nearest neighbours algorithm (kNN) is renowned
for solving classification tasks. The notion of kNN is
to seek similar data instances in the dataset as
prediction reference, for which the similarity between
instances is ordinarily measured by Euclidean distance.
Recently, some studies propose problem-tailored
distance metrics to improve the classification
performance of kNN. In this paper, we use genetic
programming to learn the transformation function, which
interprets the relationship of two data instances into
a scalar differential. The differential of data pairs
indicates the dissimilarity between two instances. This
study considers two forms of transformation functions.
Experimental results show the transform functions
learned by GP can effectively enhance the performance
of kNN.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2019.8790163",
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month = jun,
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notes = "Also known as \cite{8790163}",
- }
Genetic Programming entries for
Kuan-Chun Huang
Yu-Wei Wen
Chuan-Kang Ting
Citations