Intelligent Crossover and Mutation Technique to Control Bloat for Breast Cancer Diagnosis
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- @InProceedings{Khan:2015:CICN,
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author = "Arzoo Khan and Medhavi Chouhan",
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booktitle = "2015 International Conference on Computational
Intelligence and Communication Networks (CICN)",
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title = "Intelligent Crossover and Mutation Technique to
Control Bloat for Breast Cancer Diagnosis",
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year = "2015",
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pages = "387--391",
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abstract = "Breast cancer affects several people at present time.
Diagnosis which determines whether the cancer is benign
or malignant requires a lot of effort from doctors and
physician. Early diagnosis may save many lives.
Accurate classification plays an important role in
medical diagnosis. Genetic programming is a machine
learning algorithm which now days excelling in
classification field. But Genetic programming generally
face the problem of code bloating in which an increase
in average tree size is found without a corresponding
increase in fitness. In this paper we are proposing a
new technique for solving the problem of bloat and for
increasing classification accuracy. The technique is
known as intelligent crossover and mutation technique.
This technique is a combination of hill climbing and
conventional method which will be applied on both
crossover and mutation operator. To demonstrate this,
we had taken WBC dataset from UCI repository which has
2 classes and 9 features and we have compared
classification accuracy of our method with standard
crossover and FEDS crossover. Our classification
accuracy was 97.5percent for 50-50 training and testing
methodology 95percent for 60-40, 99percent for 70-30,
99.5percent for 80-20 and 99.6percent for 10 fold cross
validation technique. This shows our method can be used
for medical diagnosis as it provides good results.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CICN.2015.82",
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month = dec,
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notes = "Also known as \cite{7546120}",
- }
Genetic Programming entries for
Arzoo Khan
Medhavi Chouhan
Citations