摘要
将半监督学习技术与多分类器集成模型Bagging相结合,构建类别分布不平衡环境下基于Bagging的半监督集成模型(SSEBI),综合利用有、无类别标签的样本来提高模型的性能。该模型主要包括三个阶段:(1)从无类别标签数据集中选择性标记一部分样本并训练若干个基本分类器;(2)使用训练好的基本分类器对测试集样本进行分类;(3)对分类结果进行集成得到最终分类结果。在五个客户信用评估数据集上进行实证分析,结果表明本研究提出的SSEBI模型的有效性。
This paper combines semi-supervised learning with multiple classifiers ensemble model Bagging, and proposes a semi-supervised ensemble model based on Bagging for imbalanced data (SSEBI), which is expected to improve the model performance by comprehensively using samples with and without class labels. This model includes the following three pha- ses: (1) Selectively label some samples from the data set without class labels and train several base classifiers; (2) Classi- fy samples in test set by the trained base classifiers respectively; (3) Obtain the final classification results with integrating the classification results of all the base classifiers. Empirical analyses are conducted in five customer credit scoring data sets, and the results show the effectiveness of the SSEBI model.
作者
黄静
薛书田
肖进
HUANG jing XUE Shu-tian XIAO Jin(Public Administration School Business School, Sichuan University, Chengdu 610064)
出处
《软科学》
CSSCI
北大核心
2017年第7期131-134,共4页
Soft Science
基金
国家自然科学基金项目(71471124)
四川大学哲学社会科学青年学术人才基金项目(skqx201607)
四川大学一流大学建设人才人物培育工程专项(skzx2016-YCYW14)
四川大学中央高校基本科研业务费研究专项(skzx2015-sb64)