Cost-sensitive hypernetworks for imbalanced data classification

doi:10.11772/j.issn.1001-9081.2014.05.1336

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (5): 1336-1340.DOI: 10.11772/j.issn.1001-9081.2014.05.1336

• Artificial intelligence • Previous Articles Next Articles

Cost-sensitive hypernetworks for imbalanced data classification

ZHENG Yan¹,WANG Yang²,HAO Qingfeng²,GAN Zhentao³

1. School of Information Engineering, Chongqing Industry Polytechnic College, Chongqing 400050, China;
2. Chongqing Military Representative Office, Chongqing 400030, China;
3. Education Technology Center, Third Military Medical University, Chongqing 400038, China

Received:2013-11-11 Revised:2013-12-25 Online:2014-05-30 Published:2014-05-01
Contact: ZHENG Yan

用于不平衡数据分类的代价敏感超网络算法

郑燕¹,王杨²,郝青峰²,甘振韬³

1. 重庆工业职业技术学院信息工程学院，重庆 400050；
2. 重庆军代室，重庆 400030；
3. 第三军医大学教育技术中心，重庆 400038

通讯作者: 郑燕
作者简介:郑燕（1981-），女，重庆人，讲师，硕士，主要研究方向：模式识别、嵌入式技术；王杨（1980-），男，四川阆中人，工程师，硕士，主要研究方向：机器学习、物联网；郝青峰（1976-），男，陕西西安人，工程师，硕士，主要研究方向：机器学习、物联网；甘振韬（1973-），男，重庆人，高级工程师，硕士，主要研究方向：人工智能、机器学习。
基金资助:
重庆市教育委员会2010年度科学技术研究资助项目

Abstract

Abstract:

Traditional hypernetwork model is biased towards the majority class, which leads to much higher accuracy on majority class than the minority when being tackled on imbalanced data classification problem. In this paper, a Boosting ensemble of cost-sensitive hypernetworks was proposed. Firstly, the cost-sensitive learning was introduced to hypernetwork model, to propose cost-sensitive hyperenetwork model. Meanwhile, to make the algorithm adapt to the cost of misclassification on positive class, cost-sensitive hypernetworks were integrated by Boosting. The proposed model revised the bias towards the majority class when traditional hypernetwork model was tackled on imbalanced data classification, and improved the classification accuracy on minority class. The experimental results show that the proposed scheme has advantages in imbalanced data classification.

摘要：

传统的超网络模型在处理不平衡数据分类问题时，具有很大的偏向性，正类的识别率远远高于负类。为此，提出了一种代价敏感超网络Boosting集成算法。首先，将代价敏感学习引入超网络模型，提出了代价敏感的超网络模型；同时，为了使算法能够自适应正类的错分代价，采用Boosting算法对代价敏感超网络进行集成。代价敏感超网络能很好地修正传统的超网络在处理不平衡数据分类问题时过分偏向正类的缺陷，提高对负类的分类准确性。实验结果表明，代价敏感超网络Boosting集成算法具有处理不平衡数据分类问题的优势。

CLC Number:

TP18

ZHENG Yan WANG Yang HAO Qingfeng GAN Zhentao. Cost-sensitive hypernetworks for imbalanced data classification[J]. Journal of Computer Applications, 2014, 34(5): 1336-1340.

郑燕王杨郝青峰甘振韬. 用于不平衡数据分类的代价敏感超网络算法[J]. 计算机应用, 2014, 34(5): 1336-1340.

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Cost-sensitive hypernetworks for imbalanced data classification

用于不平衡数据分类的代价敏感超网络算法

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