| 基于半监督学习的客户信用评估集成模型研究 |
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| 引用本文： | 黄静,薛书田,肖进.基于半监督学习的客户信用评估集成模型研究[J].软科学,2017(7):131-134. |
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| 作者姓名： | 黄静 薛书田 肖进 |
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| 作者单位： | 1. 四川大学 公共管理学院,成都,610064;2. 四川大学 商学院,成都,610064 |
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| 基金项目： | 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) |
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| 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.
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| 关 键 词： | 信用评估 类别分布不平衡 半监督学习 Bagging 半监督集成 |
Semi-supervised Learning Based Multiple Classifiers Ensemble Model for Customer Credit Scoring |
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| HUANG jing,XUE Shu-tian,XIAO Jin.Semi-supervised Learning Based Multiple Classifiers Ensemble Model for Customer Credit Scoring[J].Soft Science,2017(7):131-134. |
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| Authors: | HUANG jing XUE Shu-tian XIAO Jin |
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| Abstract: | 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 phases: (1) Selectively label some samples from the data set without class labels and train several base classifiers;(2) Classify 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. |
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| Keywords: | credit scoring imbalanced class distribution semi-supervised learning Bagging semi-supervisedensemble |
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