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libD3C: 一种免参数的、支持不平衡分类的二类分类器

libD3C: 一种免参数的、支持不平衡分类的二类分类器. Quan Zou ( 邹 权 ) (Ph.D.& Professor) Tianjin University zouquan@tju.edu.cn. libD3C≈libSVM libSVM 的缺点 优化参数慢 处理不平衡的分类效果不好. Kernel function parameter tuning in libSVM. C=1. C=10000. Ensemble learning: Make weak classifiers to strong one.

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libD3C: 一种免参数的、支持不平衡分类的二类分类器

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  1. libD3C: 一种免参数的、支持不平衡分类的二类分类器 Quan Zou (邹 权) (Ph.D.& Professor) Tianjin University zouquan@tju.edu.cn

  2. libD3C≈libSVM • libSVM的缺点 • 优化参数慢 • 处理不平衡的分类效果不好

  3. Kernel function parameter tuning in libSVM C=1 C=10000

  4. Ensemble learning: Make weak classifiers to strong one h1( ) h2() h3( ) h4( ) h5( ) h6() h7() Classification Result Combine to form the Final strong classifier

  5. 选择性集成 选择性集成学习算法 • 改善集成学习的预测效果 • 提高集成学习的预测速度 • 降低存储需求 问题 Classifier 1 Classifier 2 Classifier n 5

  6. 基于聚类的方法 Parameters tuning Out1 Out1 Out1 Out1 Out1 Out1 C1 Out1 Out1 C1 Cluster1 C2 C2 Out2 Out2 Out2 Out2 Out2 Out2 Out2 Out2 Out2 C2 Cluster2 Data KMeans Out3 Out3 C3 C3 C3 Out3 Out3 Out3 Out3 Out3 C3 Cluster3 Out9 C9 C9 C9 C9 C9 Out9 Out9 Out9 C9 C10 Out10 Out10 Out10 Out10

  7. 模拟退火优化参数 SA

  8. Predicting in parallel

  9. Ensemble learning for Class Imbalance Problem

  10. Strategy • First, the negative set is divided randomly into several subsets equally. Every subset together with the positive set is a class balance training set. • Then several different classifiers are selected and trained with these balance training sets. They will vote for the last prediction when facing new samples. • The samples will be added to the next two classifiers’ training sets if they are misclassified. • Reference • 邹权, 郭茂祖, 刘扬, 王峻. 类别不平衡的分类方法及在生物信息学中的应用. 计算机研究与发展. 2010,47(8):1407-1414 • X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550

  11. http://lab.malab.cn/soft/LibD3C/

  12. http://lab.malab.cn/soft/MRMD/ Quan Zou, et al. A Novel Features Ranking Metric with Application to Scalable Visual and Bioinformatics Data Classification. Neurocomputing. 2016, 173:346-354

  13. ESI High Cited Paper

  14. Application in Bioinformatics • DNA Binding proteins • Li Song, Dapeng Li, Xiangxiang Zeng, Yunfeng Wu, Li Guo*, Quan Zou*. nDNA-prot: Identification of DNA-binding Proteins Based on Unbalanced Classification. BMC Bioinformatics. 2014, 15:298. ESI高引论文(HIGHLY CITED PAPER) • Cytokines • Quan Zou, et al. An Approach for Identifying Cytokines Based On a Novel Ensemble Classifier. BioMed Research International. 2013, 2013:686090 • tRNA • Quan Zou, et al. Improving tRNAscan-SE annotation results via ensemble classifiers. Molecular Informatics. 2015,34(11-12):761-770 • miRNA • Leyi Wei, Minghong Liao, Yue Gao, Rongrong Ji, Zengyou He*, Quan Zou*. Improved and Promising Identification of Human MicroRNAs by Incorporating a High-quality Negative Set. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014, 11(1):192-201 ESI高引论文(HIGHLY CITED PAPER)

  15. 利用邹权副教授提出的集成学习方法

  16. http://lab.malab.cn/soft/LibD3C/ Thanks ! • Quan Zou（PH.D., Prof.） • School of Computer Sci &Tech@Tianjin Univ. • Email：zouquan@nclab.net • http://lab.malab.cn/

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