Combining ensemble of classifiers using voting-based rule to predict radiological ratings for lung nodule malignancy

Vinay, K. and Rao, A. and Hemantha Kumar, G. (2014) Combining ensemble of classifiers using voting-based rule to predict radiological ratings for lung nodule malignancy. In: Emerging Research in Electronics, Computer Science and Technology.

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Official URL: https://doi.org/10.1007/978-81-322-1157-0_45

Abstract

In this paper, we are proposing new ensemble strategy for classification of lung nodules based on their malignancy ratings. The procedure we followed is simpler. In the first step, we construct different homogenous ensemble models such as bagged decision tree (BaDT), boosted decision tree (BoBT), and random subspace-based decision tree (RSSDT). In the next step, we combine previously constructed models with voting scheme to yield ensemble of homogenous ensemble of classifiers. We also examine the behavior of our method for heterogeneity in the system. This is done by constructing ensemble of heterogeneous ensemble of classifiers. For this, we have also considered bagged KNN (BaKNN), boosted KNN (BoKNN), bagged PART (BaPART), and boosted PART classifier (BoPART). The results we are obtaining from our strategy are significant compared to homogenous ensemble model.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Decision trees, Data mining, Computer science, Bagging, Biological organs, Boosting, Ensemble of classifiers, KNN, PART, Random subspaces
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Arshiya Kousar
Date Deposited: 10 Jul 2019 07:04
Last Modified: 19 Aug 2019 05:09
URI: http://eprints.uni-mysore.ac.in/id/eprint/4475

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