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Classifiers in Context: Prediction of Radiological Characteristic Ratings for Lung Nodule Malignancy

Vinay. K, Ashok Rao, G. Hemantha Kumar Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
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  1. Vinay. K, Ashok Rao and Hemantha G Kumar. Article: Classifiers in Context: Prediction of Radiological Characteristic Ratings for Lung Nodule Malignancy. International Journal of Applied Information Systems 5(2):14-19, January 2013. BibTeX

    	author = "Vinay. K and Ashok Rao and G. Hemantha Kumar",
    	title = "Article: Classifiers in Context: Prediction of Radiological Characteristic Ratings for Lung Nodule Malignancy",
    	journal = "International Journal of Applied Information Systems",
    	year = 2013,
    	volume = 5,
    	number = 2,
    	pages = "14-19",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"


In this paper, we are exploring a panel of classifier response to an imbalanced medical data set. In this work we are using LIDC (Lung Image Database Consortium) dataset, which is a very good example for imbalanced data. The main objective of this work is to examine how the response of different categories of classifier is, when subjected to imbalanced dataset. We are considering five categories of classifiers which are grouped as, Instance Based classifier, Rule Based classifiers, Functional Classifier, Decision Tree classifier and Ensemble of Classifiers. The results from our experiments will be evaluated based on performance metrics such as Accuracy, Precision, Recall, F-measure, Area under curve and Kappa statistics.


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Ensemble of classifiers, Decision Tree, Kappa Statistics