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A Function, Bayes, Meta And Tree Classifier Perspective Based On Ilpd Dataset
[Full Text]
AUTHOR(S)
Prasun Chakrabarti, Manish Tiwari, Aditya Maheshwari, Tulika Chakrabarti, Sibabrata Mukhopadhyay
KEYWORDS
accuracy, precision, function, Bayes, Meta, tree, classifiers
ABSTRACT
Several discovered research findings entail that for the liver cancer detection, supervised machine learning approaches play a pivotal role. In this paper the classifiers performance parameters such as accuracy and precision are used for analysis purpose in the light of Function, Bayes, Meta and Tree Classifiers.
REFERENCES
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[2] Tiwari M., Chakrabarti P. and Chakrabarti T., “Performance Vector analysis in context to liver cancer – A Support Vector Machine Approach with a survey on the latest Perspectives of Chemistry in liver cancer treatment”, International Journal of Computer Science and Information Security, 14(9),pp.1238-1242,2016.
[3] Tiwari M., Chakrabarti P and Chakrabarti T., “Performance analysis and error evaluation towards the liver cancer diagnosis using lazy classifiers for ILPD”, Communications in Computer and Information Science ,837, pp.161-168,2018.
[4] Tiwari M., Chakrabarti P and Chakrabarti T., “Novel work of diagnosis in liver cancer using Tree classifier on liver cancer dataset ( BUPA liver disorder )” , Communications in Computer and Information Science , 837, pp.155-160, 2018.
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[6] Kahramanli H., Allahverdi N., A system for detection of Liver Disorders based on Adaptive Neural Networks and Artificial Immune System Proceedings of the 8th WSEAS international conference on applied compurer science pp 25-30, 2008.
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