Regularized Weighted Ensemble of Deep Classifiers

Ensemble of classifiers increases the performance of the classification since the decision of many experts are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research. Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS, Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level deep classifier ensemble gives the best result in our experiments.