Prediction of malignancy degree in brain glioma using selective neural networks ensemble

Abstract

A clustering algorithm based selective neural networks ensemble (CLUSEN) is proposed to predict the degree of malignancy in brain glioma. Since the degree prediction of malignancy is critical before brain surgery, many learning methods are used like rule induction algorithm, single neural networks, support vector machines, etc. Ensemble learning methods can improve the generalization of single learning machine, and are becoming popular in the machine learning and medical data processing communities. The procedure of CLUSEN can efficiently remove redundancy learning individuals and help improve the diversity of ensemble methods. CLUSEN is used to predict the degree of malignancy in brain glioma. Experimental results on a set of brain glioma data show that, compared to support vector machines, rule induction and single neural networks, the classification accuracy of CLUSEN is higher.