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SCIENTIA SINICA Informationis, Volume 47 , Issue 11 : 1483-1492(2017) https://doi.org/10.1360/N112017-00106

Model reuse with domain knowledge

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  • ReceivedMay 16, 2017
  • AcceptedMay 22, 2017
  • PublishedNov 14, 2017

Abstract


Funded by

国家自然科学基金(61333014)


References

[1] Zhou Z H. Learnware: on the future of machine learning. Front Comput Sci, 2016, 10: 589-590 CrossRef Google Scholar

[2] 周志华. 机器学习: 发展与未来. 中国计算机学会通讯 2017, 13: 44--51. Google Scholar

[3] Pan S J, Yang Q. A Survey on Transfer Learning. IEEE Trans Knowl Data Eng, 2010, 22: 1345-1359 CrossRef Google Scholar

[4] Jiang J. A literature survey on domain adaptation of statistical classifiers. 2008. http://sifaka.cs.uiuc.edu/jiang4/domain_adaptation/survey/da_survey.pdf. Google Scholar

[5] Blitzer J, McDonald R, Pereira F. Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing Sydney, 2006. 120--128. Google Scholar

[6] Glorot X, Bordes A, Bengio Y. Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning Bellevue, 2011. 513--520. Google Scholar

[7] Luo J, Tommasi T, Caputo B. Multiclass transfer learning from unconstrained priors. In: Proceedings of the 2011 International Conference on Computer Vision Washington, 2011. 1863--1870. Google Scholar

[8] Da Q, Yu Y, Zhou Z H. Learning with augmented class by exploiting unlabeled data. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence Quebec City, 2014. 1760--1766. Google Scholar

[9] Mu X, Zhu F D, Du J, et al. Streaming classification with emerging new class by class matrix sketching. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, 2017. 2373--2379. Google Scholar

[10] Zhu Y, Ting K M, Zhou Z H. Multi-label learning with emerging new labels. In: Proceedings of the 16th International Conference on Data Mining Barcelona, 2016. 1371--1376. Google Scholar

[11] Zhu Y, Ting K M, Zhou Z H. Discover multiple novel labels in multi-instance multi-label learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence San Francisco, 2017. 2977--2984. Google Scholar

[12] Li N, Tsang I W, Zhou Z H. Efficient optimization of performance measures by classifier adaptation.. IEEE Trans Pattern Anal Mach Intell, 2013, 35: 1370-1382 CrossRef PubMed Google Scholar

[13] Yang Y, Zhan D C, Fan Y, et al. Deep learning for fixed model reuse. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence San Francisco, 2017. 2831--2837. Google Scholar

[14] Zhao P, Jiang Y, Zhou Z H. Multi-view matrix completion for clustering with side information. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining Jeju, 2017. 403--415. Google Scholar

[15] Dai W Z, Zhou Z H. Combining logical abduction and statistical induction: discovering written primitives with human knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence San Francisco, 2017. 4392--4398. Google Scholar

[16] Li Fei-Fei , Fergus R, Perona P. One-shot learning of object categories.. IEEE Trans Pattern Anal Machine Intell, 2006, 28: 594-611 CrossRef PubMed Google Scholar

[17] Palatucci M, Pomerleau D, Hinton G E, et al. Zero-shot learning with semantic output codes. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems Vancouver, 2009. 1410--1418. Google Scholar

[18] Lampert C H, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization.. IEEE Trans Pattern Anal Mach Intell, 2014, 36: 453-465 CrossRef PubMed Google Scholar

[19] Mei S, Zhu J, Zhu J. Robust regbayes: selectively incorporating first-order logic domain knowledge into bayesian models. In: Proceedings of the 31st International Conference on Machine Learning Beijing, 2014. 253--261. Google Scholar

[20] Fu Y W, Sigal L. Semi-supervised vocabulary-informed learning. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, 2016. 5337--5346. Google Scholar

[21] Radivojac P, Clark W T, Oron T R. A large-scale evaluation of computational protein function prediction.. Nat Meth, 2013, 10: 221-227 CrossRef PubMed Google Scholar

[22] The UniProt Consortium Uniprot: a hub for protein information. Nucleic Acids Res 2014, 43: D204. Google Scholar

[23] Ashburner M, Ball C A, Blake J A. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.. Nat Genet, 2000, 25: 25-29 CrossRef PubMed Google Scholar

[24] Ofer D, Linial M. ProFET: Feature engineering captures high-level protein functions.. Bioinformatics, 2015, 31: 3429-3436 CrossRef PubMed Google Scholar

[25] Zhou Z H, Zhang M L. Multi-instance multi-label learning with application to scene classification. In: Proceedings of the 19th International Conference on Neural Information Processing Systems Cambridge: MIT Press, 2006. 1609--1616. Google Scholar

[26] Chua T S, Tang J H, Hong R C, et al. Nus-wide: a real-world web image database from national university of singapore. In: Proceedings of the ACM International Conference on Image and Video Retrieval Santorini, 2009. Google Scholar

[27] Lin T Y, Maire M, Belongie S J, et al. Microsoft COCO: common objects in context. In: Proceedings of the 13th European Conference on Computer Vision, Zurich, 2014. 740--755. Google Scholar

[28] Gao B B, Xing C, Xie C W. Deep Label Distribution Learning With Label Ambiguity. IEEE Trans Image Process, 2017, 26: 2825-2838 CrossRef PubMed ADS arXiv Google Scholar

[29] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Comput Vision Recogn 2014,. arXiv Google Scholar

[30] Pennington J, Socher R, Manning C D. Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing Doha, 2014. 1532--1543. Google Scholar

  • Figure 1

    The illustration of the MRDK framework

  • Figure 2

    The data and model flow of MRDK

  • Figure 3

    Part of the ancestor chart of endodeoxyribonuclease activity

  • Table 1   Proteome datasets information
    Domain Proteome #Instance #Class Label cardinality
    BacteriaGEOSL 378 319 3.143
    AZOVD 406 340 3.993
    ArchaeaHALMA 304 234 3.247
    PYRFU 425 321 4.480
    Eukaryota YEAST 3507 1566 5.887
  • Table 2   Results of protein function prediction
    Proteome Hamming loss $\downarrow$F-measure $\uparrow$
    $f_0$ $f^+$ $f_0$ $f^+$
    GEOSL0.0700.0230.0640.124
    AZOVD0.0960.0240.0350.071
    HALMA0.0350.0170.0970.175
    PYRFU0.0220.0170.1730.183
    YEAST0.1080.0090.0120.080
  • Table 3   Information of image datasets
    Dataset #Instance #Class Label cardinality
    Scene 2000 5 1.236
    VOC07 9963 20 1.437
    MS-COCO 122218 80 2.926
    NUS-WIDE 133441 81 1.761
  • Table 4   Results of image classification
    Dataset Hamming loss $\downarrow$F-measure $\uparrow$
    $f_0$ $f^+$ $f_0$ $f^+$
    Scene0.1600.1520.6850.703
    VOC070.0560.0470.6230.665
    MS-COCO0.0400.0350.4410.454
    NUS-WIDE0.0350.0290.1980.196