Abstract

In this paper, we are exploring the response of individual classifier families on imbalanced medical data. 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 will be the response of different categories of classifier on imbalanced dataset. We are considering five categories of dataset 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 following performance metrics such as Accuracy, Precision, Recall, F-measure, Area under curve and kappa statistics.

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