Success of ensemble algorithms in classification of electrical impadence spectroscopy breast tissue records

In this study was performed by using records from breast tissue electrical impedance spectroscopy analysis. The aim of the study is to reveal the impact of ensemble algorithms on success of the classification performance in the classification of normal and pathological breast tissue classification. For this purpose have been used three different ensemble algorithms they are bagging, adaboost, random subspaces and three main basic classifiers, which are RF, YSA, DVM. The results obtained are supplemented with performance analysis and ensemble algorithms have been demonstrated to increase classification performance results. The results obtained by the combined use of adaboost ensemble algorithm with RF basic classifier demonstrate, that the success rate was higher than the others (%89.62).