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.
The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-yl）-2-（2,4-difluorophenyl）-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors： net atomic charge on the atom N connecting with R, dipole moment and heat of formation, By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation （LOOCV） test, The results indicate that the performance of the SVC model can exceed that of the principal component analysis （PCA） and K-Nearest Neighbor （KNN） models for this real world data set.
在大脑胶质瘤诊断数据集中,病例样本数通常比正常样本数要少,由此引发了数据不均衡问题下病例诊断的问题。此外,在大脑胶质瘤数据集中有一些冗余甚至是不相关的特征,这些特征降低了学习器的泛化能力。为解决这类问题,提出一种基于互信息特征选择的E asyEnsem b le算法来解决大脑胶质瘤诊断中的数据不均衡问题。在UC I数据集和大脑胶质瘤数据集上的实验结果表明新算法提高了分类器在不均衡数据集上的分类性能和预报能力。
