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SCIENTIA SINICA Informationis, Volume 50 , Issue 6 : 794-812(2020) https://doi.org/10.1360/SSI-2019-0208

An interactive feature selection method based on learning-from-crowds

More info
  • ReceivedSep 24, 2019
  • AcceptedDec 9, 2019
  • PublishedJun 9, 2020

Abstract


Funded by

国家重点研发计划(2018YFB1004300)

国家自然科学基金(61672308,61761136020,61936002)


References

[1] Guyon I, Andre E. An introduction to variable and feature selection. Journal of Machine Learning Research, 2009, 3(Mar): 1157-1182. Google Scholar

[2] Saeys Y, Abeel T, van de Peer Y. Robust feature selection using ensemble feature selection techniques. In: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Antwerp, 2008. 313--325. Google Scholar

[3] Wang H, Khoshgoftaar T M, Napolitano A. A comparative study of ensemble feature selection techniques for software defect prediction. In: Proceedings of the International Conference on Machine Learning and Applicatioin, Hyatt Regency Bethesda, 2010. 135--140. Google Scholar

[4] Li X, Zhang T W, Guo Z. A Novel Ensemble Method of Feature Gene Selection Based on Recursive Partition-Tree (in Chinese). Chinese Journal of Computers, 2004, 27(5): 675-682. Google Scholar

[5] Bolón-Canedo V, Sánchez-Maro?o N, Alonso-Betanzos A. Distributed feature selection: An application to microarray data classification. Appl Soft Computing, 2015, 30: 136-150 CrossRef Google Scholar

[6] Netzer M, Millonig G, Osl M. A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry.. Bioinformatics, 2009, 25: 941-947 CrossRef PubMed Google Scholar

[7] Feng Yang , Mao K Z. Robust feature selection for microarray data based on multicriterion fusion.. IEEE/ACM Trans Comput Biol Bioinf, 2011, 8: 1080-1092 CrossRef PubMed Google Scholar

[8] Tian T, Zhu J. Max-Margin Majority Voting for learning from crowds. In: Proceedings of Advances in Neural Information Processing Systems. Palais des Congrés de Montréal, 2015. 1621--1629. Google Scholar

[9] Liu M, Jiang L, Liu J, et al. Improving learning-from-crowds through expert validation. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, 2017. 2329--2336. Google Scholar

[10] Hanchuan Peng , Fuhui Long , Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.. IEEE Trans Pattern Anal Machine Intell, 2005, 27: 1226-1238 CrossRef PubMed Google Scholar

[11] Guo D. Coordinating Computational and Visual Approaches for Interactive Feature Selection and Multivariate Clustering. Inf Visualization, 2003, 2: 232-246 CrossRef Google Scholar

[12] MacEachren A, Xiping D, Hardisty F, et al. Exploring high-D spaces with multiform matrices and small multiples. In: Proceedings of the IEEE Symposium on Information Visualization, Seattle, 2003. 31--38. Google Scholar

[13] Ingram S, Munzner T, Irvine V, et al. Dimstiller: Workflows for dimensional analysis and reduction. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Salt Lake City, 2010. 3--10. Google Scholar

[14] Yang J, Patro A, Huang S, et al. Value and relation display for interactive exploration of high dimensional datasets. In: Proceedings of the IEEE Symposium on Information Visualization, Austin, 2004. 73--80. Google Scholar

[15] Lin H, Gao S, Gotz D, et al. RCLens: Interactive rare category exploration and identification. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(7), 2223-2237. Google Scholar

[16] Seo J, Shneiderman B. A Rank-by-Feature Framework for Interactive Exploration of Multidimensional Data. Inf Visualization, 2005, 4: 96-113 CrossRef Google Scholar

[17] Piringer H, Berger W, Hauser H. Quantifying and comparing features in high-dimensional datasets. In: Proceedings of the International Conference Information Visualisation, London, 2008. 240--245. Google Scholar

[18] May T, Bannach A, Davey J, et al. Guiding feature subset selection with an interactive visualization. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Providence, 2011. 111--120. Google Scholar

[19] Johansson S, Johansson J. Interactive dimensionality reduction through user-defined combinations of quality metrics.. IEEE Trans Visual Comput Graphics, 2009, 15: 993-1000 CrossRef PubMed Google Scholar

[20] Krause J, Perer A, Bertini E. INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data.. IEEE Trans Visual Comput Graphics, 2014, 20: 1614-1623 CrossRef PubMed Google Scholar

[21] Brooks M, Amershi S, Lee B, et al. FeatureInsight: Visual support for error-driven feature ideation in text classification. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, Chicago, 2015. 105--112. Google Scholar

[22] Liu S, Xiao J, Liu J, et al. Visual diagnosis of tree boosting methods. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 123-132. Google Scholar

[23] Zhou Z. Abductive learning: towards bridging machine learning and logical reasoning. Science China - Information Sciences. 2019, 62(7): 076101:1-076101:1-3. Google Scholar

[24] Xiao J, Liu M, Liu S. A Visual Analysis System for News Data (in Chinese). Journal of Computer-Aided Design & Computer Graphics, 2016, 28(11): 1863-1871. Google Scholar

[25] Wu Y, Cui W, Song Y, et al. A Survey on Topic-Based Visual Text Analytics (in Chinese). Journal of Computer-Aided Design & Computer Graphics, 2012, 24(10): 1266-1272. Google Scholar

[26] Zhu J, Ning C, Eric P Xing. Bayesian inference with posterior regularization and applications to infinite latent SVMs. Journal of Machine Learning Research, 2014, 15(1): 1799-1847. Google Scholar

[27] Donahue J, Jia Y, Vinyals O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition. In: Proceedings of the International Conference on Machine Learning, Beijing, 2014. 647--655. Google Scholar

[28] Jiang L, Liu S, Chen C. Recent research advances on interactive machine learning. Journal of Visualization. 2018, Nov(12):1-17. Google Scholar

[29] Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 2921--2929. Google Scholar

[30] Lang K. Newsweeder: learning to filter netnews. In: Proceedings of the International Conference on Machine Learning, Tahoe City, 1995. 331--339. Google Scholar

[31] Han E H S, Karypis G. Centroid-based document classification: analysis and experimental results. In: Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, 2000. 424--431. Google Scholar

[32] Joachims T. Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the European Conference on Machine Learning, Chemnitz, 1998. 137--142. Google Scholar

[33] Nene S A, Nayar S K, Murase H. Columbia Object Image Library. Technical Report CUCS-005-96, 1996. Google Scholar

[34] Vapnic V. The Nature of Statistical Learning Theory. Berlin: Springer Science&Business Media, 1995. Google Scholar

[35] Krizhevsky A. Learning Multiple Layers of Features From Tiny Images. Technical Report, 2009. Google Scholar