计算机科学 ›› 2017, Vol. 44 ›› Issue (6): 212-215.doi: 10.11896/j.issn.1002-137X.2017.06.035
强保华,唐波,王玉峰,邹显春,柳正利,孙忠旭,谢武
QIANG Bao-hua, TANG Bo, WANG Yu-feng, ZOU Xian-chun, LIU Zheng-li, SUN Zhong-xu and XIE Wu
摘要： 对于高维度小样本数据的分类问题,高维属性的复杂性限制了分类模型预测的准确率。为了进一步提高准确率,提出了基于线性回归和属性集成的分类算法。首先,采用线性回归为每一个属性构建属性线性分类器(Attri-bute Linear Classifier,ALC)；其次,为了避免因ALC数量过多而导致准确率下降,利用经验风险最小化策略中的经验损失值作为评估标准来优选ALC；最后,应用多数投票法来集成被筛选的ALC。采用高维度小样本的基因表达数据集进行实验,结果显示该算法具有比逻辑回归、支持向量机和随机森林算法更高的准确率。
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