Combining Ensemble of Classifiers Using Voting-Based Rule to Predict Radiological Ratings for Lung Nodule Malignancy

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.