Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Ain:2018:PRICAI,
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author = "Qurrat {Ul Ain} and Bing Xue and Harith Al-Sahaf and
Mengjie Zhang",
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title = "Genetic Programming for Feature Selection and Feature
Construction in Skin Cancer Image Classification",
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booktitle = "PRICAI 2018: Trends in Artificial Intelligence - 15th
Pacific Rim International Conference on Artificial
Intelligence, Proceedings, Part I",
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year = "2018",
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editor = "Xin Geng and Byeong-Ho Kang",
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volume = "11012",
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series = "Lecture Notes in Computer Science",
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pages = "732--745",
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address = "Nanjing, China",
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month = aug # " 28-31",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-97303-6",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/pricai/pricai2018a.html#AinXAZ18",
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DOI = "doi:10.1007/978-3-319-97304-3_56",
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abstract = "The incidence of skin cancer, particularly, malignant
melanoma, continues to increase worldwide. If such a
cancer is not treated at an early stage, it can be
fatal. A computer system based on image processing and
computer vision techniques, having good diagnostic
ability, can provide a quantitative evaluation of these
skin cancer cites called skin lesions. The size of a
medical image is usually large and therefore requires
reduction in dimensionality before being processed by a
classification algorithm. Feature selection and
construction are effective techniques in reducing the
dimensionality while improving classification
performance. This work develops a novel genetic
programming (GP) based two-stage approach to feature
selection and feature construction for skin cancer
image classification. Local binary pattern is used to
extract gray and colour features from the dermoscopy
images. The results of our proposed method have shown
that the GP selected and constructed features have
promising ability to improve the performance of
commonly used classification algorithms. In comparison
with using the full set of available features, the GP
selected and constructed features have shown
significantly better or comparable performance in most
cases. Furthermore, the analysis of the evolved feature
sets demonstrates the insights of skin cancer
properties and validates the feature selection ability
of GP to distinguish between benign and malignant
cancer images.",
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notes = "conf/pricai/AinXAZ18",
- }
Genetic Programming entries for
Qurrat Ul Ain
Bing Xue
Harith Al-Sahaf
Mengjie Zhang
Citations