用于不平衡数据分类的代价敏感超网络算法

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。

Cost-sensitive hypernetworks for imbalanced data classification

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.