Enhancing Decision Tree Classification Accuracy through Genetically Programmed Attributes for Wart Treatment Method Identification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{KHATRI:2018:PCS,
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author = "Sabita Khatri and Deepak Arora and Anil Kumar",
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title = "Enhancing Decision Tree Classification Accuracy
through Genetically Programmed Attributes for Wart
Treatment Method Identification",
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journal = "Procedia Computer Science",
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volume = "132",
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pages = "1685--1694",
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year = "2018",
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note = "International Conference on Computational Intelligence
and Data Science",
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keywords = "genetic algorithms, genetic programming, Warts,
Immunotherapy, Machine Learning, Decision Tree",
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ISSN = "1877-0509",
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DOI = "doi:10.1016/j.procs.2018.05.141",
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URL = "http://www.sciencedirect.com/science/article/pii/S1877050918308731",
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abstract = "Origin: Warts are produced and developed on the human
body due to infection induced by Human Papillomavirus.
The most influenced zone of warts are hands and feet
particularly, which is bit irritating and difficult to
recoup in later stages. The major challenge in treating
warts is the diversity of treatment method applicable
on different patients, so it becomes difficult to
recognize specific treatment method to be adopted in
order to treat this infection. Ramifications of machine
learning techniques in the medical domain have become
crucial nowadays for early disease detection and
developing expert systems. Objective: This research
work focuses on enhancing predictive accuracy of J48,
which is a binary decision tree based classifier by
adding attributes based on genetic programming. These
genetically tuned attribute construction not only just
upgrades the classification capabilities of J48
classifier but also additionally expand the information
space, intending J48 for giving more exact predictions
for wart treatment method identification. Method: For
their experimental setup, authors have chosen
immunotherapy and cryotherapy datasets from UCI machine
learning repositories, which includes instances of
patients responses against treated with immunotherapy
and cryotherapy methods for both plantar and common
warts. The investigation has been led with the help of
WEKA tool, which is an open source for performing data
mining operations. Finding: After experimentation, it
is found after inclusion of attributes generated
through genetic programming, the classification
accuracy of J48 can be increased by a substantial
amount with less error rate. The result shows
significant performance improvements in classification
accuracy of J48 by 82.22percent to 96.66percent and
93.33percent to 98.88percent for immunotherapy and
cryotherapy datasets, implemented with J48 and J48+GA
respectively",
- }
Genetic Programming entries for
Sabita Khatri
Deepak Arora
Anil Kumar
Citations