ISSN 1000-1239 CN 11-1777/TP

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类别不平衡的分类方法及在生物信息学中的应用

邹 权 郭茂祖 刘 扬 王 峻   

  1. (哈尔滨工业大学计算机科学与技术学院 哈尔滨 150001) (zouquan@xmu.edu.cn)
  • 出版日期: 2010-08-15

A Classification Method for Class-Imbalanced Data and Its Application on Bioinformatics

Zou Quan, Guo Maozu, Liu Yang, and Wang Jun   

  1. (School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001)
  • Online: 2010-08-15

摘要: 提出一种处理正反例不平衡的分类方法,以解决生物信息学中的snoRNA识别、microRNA前体判别、SNP位点的真伪识别等问题. 利用集成学习的思想,将反例集均匀分割并依次与正例集组合,得到一组类别平衡的训练集.然后对每个训练集采用不同原理的分类器进行训练,最后投票表决待测样本.为了避免弱分类器影响投票效果,结合AdaBoost思想,将每个分类器训练中产生的错误样本加入到下2个分类器的训练集中,既避免了AdaBoost的反复训练,又有效地利用投票机制遏制了弱分类器的影响.5组UCI测试数据和3组生物信息学实验证明了它在处理类别不平衡分类问题时的优越性.

关键词: 生物信息学, 类别不平衡, 非编码RNA识别, SNP位点鉴别, 分类

Abstract: A classification method is proposed for class-imbalanced data, which is common in bioinformatics, such as identifying snoRNA, classifying microRNA precursors from pseudo ones, mining SNPs from EST sequences, etc. It is based on the main idea of ensemble learning. First, the big class set is divided randomly into several subsets equally, and it is made sure that every subset together with the small class set can make up a class-balanced training set. Then several different mechanism classifiers are selected and trained with these balanced training sets. After the multi-classifiers are built, they will vote for the last prediction when dealing with new samples. In the training phase, a strategy similar to AdaBoost is used. For each classifier, the samples will be added to the training sets of next two classifiers if they are misclassified. It is necessary to repeat modifying the training sets until a classifier can accurately predict its training set or reaching the maximum repeat times. This strategy can improve the performance of weak classifiers by voting. Experiments on five UCI data sets and three bioinformatics experiments mentioned above prove the performance of the method. Furthermore, a software program named LibID, which can be used as similarly as LibSVM, is developed for the researchers from bioinformatics and other fields.

Key words: bioinformatics, class imbalance, ncRNA identification, mining SNP from EST, classification