AdaBoost Ensemble Data Classification based on Diversity of Classifiers

In this research we propose an ensemble classification technique based on decision tree, artificial neural network, and support vector machine models weighting classifier by adaboost in order to increase classification accuracy. we used a total of 30 classifiers. The technique generated random data used Bootstrap. Testing Diabites Data from UCI, classification accuracy tests on Diabites Data found that the proposed ensemble classification models weighting classifier by Adaboost yields better performance than that of a single model with the same type of classifier. The result as follows, Diabites Data achieved the best performance with 75.21%. we can conclude that there are two essential requirements in the model. The first is that the ensemble Members or learning agents must be diverse or complementary, i.e., agents must exhibit different properties. Another condition is that an optimal ensemble strategy is also required to fuse a set of diverse by AdaBoost.