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.
In this paper we are exploring a novel approach to extracting the features from a hand-written offline signature. The experiments are carried out on a user created data base. Which includes 50 classes and each class consists of 15 random forgeries and 5 skilled forgeries; total count of samples in the data base is 1000. Here we are considering and extracting the geometrical distance metric features, end points alignment and pruned projection features, vector of angle and one existing SIFT features. To obtain the similarity matrix we apply the different technique called one-to-one string pattern matching. The simple measure of similarity R itself is serve as a feature. Late the patterns are classified using one supervised classifier like Knnclassifier the results are compared. In biometric experiments accuracy should be measured in terms of equal error rates. We are comparing the results of existing and proposed methods and justifying the results. For verification and identification error rates are 1.2% and 1.4% by novel approach and that of existing SIFT feature is 1.4% and 1.6%.
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