Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis
Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis
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DOI：
10.1016/j.cie.2019.106266
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发表时间：
2020-02-01
影响因子：
7.9
通讯作者：
Liu, Na
中科院分区：
文献类型：
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作者：
Gan, Dan;Shen, Jiang;Liu, Na
对于不平衡分类问题，传统的分类模型大多只考虑在固定的误分类代价下寻找最优分类器以最大化分类精度，而没有考虑误分类代价随样本概率分布的变化。代价敏感学习方法可以有效地解决不平衡数据分类问题。在这方面，我们提出了一个集成的TANBN与成本敏感的分类算法（AdaC-TANBN），以克服上述缺点，提高分类精度。AdaC-TANBN算法采用由样本分布概率确定的可变误分类代价训练分类器，实现对医学诊断中不平衡数据的分类。我们提出的方法的有效性在克利夫兰心脏数据集（心脏），印度肝脏患者数据集（ILPD），皮肤病数据集和宫颈癌风险因素数据集（CCRF）上进行了检查。实验结果表明，AdaC-TANBN算法的性能优于其他国家的最先进的比较方法。
For the imbalanced classification problems, most traditional classification models only focus on searching for an excellent classifier to maximize classification accuracy with the fixed misclassification cost, not take into consideration that misclassification cost can change with sample probability distribution. So far as we know, cost-sensitive learning method can be effectively utilized to solve imbalanced data classification problems. In this regards, we propose an integrated TANBN with cost-sensitive classification algorithm (AdaC-TANBN) to overcome the above drawback and improve classification accuracy. The AdaC-TANBN algorithm employs variable misclassification cost determined by samples distribution probability to train classifier, then implements classification for imbalanced data in medical diagnosis. The effectiveness of our proposed approach is examined on the Cleveland heart dataset (Heart), Indian liver patient dataset (ILPD), Dermatology dataset and Cervical cancer risk factors dataset (CCRF) from the UCI learning repository. The experimental results indicate that the AdaC-TANBN algorithm can outperform other state-of-the-art comparative methods.