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Online Multi-label Feature Selection on Imbalanced Data Sets

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Wireless Sensor Networks (CWSN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 812))

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Abstract

Feature selection is an important step of data processing. When feature selection is conducted for multi-label classification problem in online learning fashion, it is the problem of online multi-label feature selection. Online feature selection is very appropriate for some actual situations in which the data is not available in advance, the data size is very large or fast running speed is highly demanding. We propose an online multi-label feature selection algorithm in which the data set is divided into many single-label data sets, feature selection is conducted for each single-label data set and the final features are selected from the selected single-label features. As many data sets are imbalanced, we use the basic idea of cost-sensitive learning to combat it. Experiment results corroborate the performance of our algorithm on various data sets and demonstrate that the proposed algorithm can improve online classification performance on imbalanced data sets effectively.

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References

  1. Li, H., Xu, X., Lai, L., Shen, Y.: Online commercial intention detection framework based on web pages. Int. J. Comput. Sci. Eng. 12(2/3), 176–185 (2016)

    Article  Google Scholar 

  2. Perozzi, B., Al-Rfou, R., Skiena, S: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)

    Google Scholar 

  3. Rosenblatt, F.: The perception: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)

    Article  Google Scholar 

  4. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7(3), 551–585 (2006)

    MathSciNet  MATH  Google Scholar 

  5. Cesabianchi, N., Conconi, A., Gentile, C.: A second-order perceptron algorithm. SIAM J. Comput. 2375(3), 121–137 (2002)

    MathSciNet  MATH  Google Scholar 

  6. Wang, J., Zhao, P., Hoi, S.C.H.: Exact soft confidence-weighted learning. In: Computer Science, pp. 107–114 (2012)

    Google Scholar 

  7. Crammer, K., Dredze, M., Pereira, F.: Confidence-weighted linear classification for text categorization. J. Mach. Learn. Res. 13(1), 1891–1926 (2012)

    MathSciNet  MATH  Google Scholar 

  8. Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)

    Article  Google Scholar 

  9. Dash, M., Gopalkrishnan, V.: Distance based feature selection for clustering microarray data. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds.) DASFAA 2008. LNCS, vol. 4947, pp. 512–519. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78568-2_41

    Chapter  Google Scholar 

  10. Karegowda, A.G., Bharathi, P.T.: Enhancing cbir performance using evolutionary algorithm-assisted significant feature selection: a filter approach. Int. J. Appl. Res. Inf. Technol. Comput. 7(1), 53–59 (2016)

    Article  Google Scholar 

  11. Rodrigues, D., Nakamura, R.Y.M., Costa, K.A.P., Yang, X.S.: A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst. Appl. 41(5), 2250–2258 (2014)

    Article  Google Scholar 

  12. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)

    Article  Google Scholar 

  13. Li-Yeh, C., Ke, C.H., Yang, C.H.: A hybrid both filter and wrapper feature selection method for microarray classification. In: International Multi Conference of Engineers and Computer Scientists, vol. 2168 (2008)

    Google Scholar 

  14. Longadge, R., Dongre, S.: Class imbalance problem in data mining review. Int. J. Comput. Sci. Netw. 2(1), 83 (2013)

    Google Scholar 

  15. Wang, J., Zhao, P., Hoi, S.C.H., Jin, R.: Online feature selection and its applications. IEEE Trans. Knowl. Data Eng. 26(3), 698–710 (2013)

    Article  Google Scholar 

  16. Mulan. http://mulan.sourceforge.net/datasetsmlc.html

  17. UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets.html

  18. Han, C., Tan, Y.K., Zhu, J.H., et al.: Online feature selection of class imbalance via PA algorithm. J. Comput. Sci. Technol. 31(4), 673–682 (2016)

    Article  MathSciNet  Google Scholar 

  19. Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: International Conference on Machine Learning, pp. 179–186 (1997)

    Google Scholar 

  20. Chen, X.W., Wasikowski, M.: FAST:a ROC-based feature selection metric for small samples and imbalanced data classification problems. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 46, pp. 124–132 (2008)

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under the grant number 61379127, 61379128.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

    Jing Liu, Zhongwen Guo, Zhongwei Sun, Shiyong Liu & Xupeng Wang

Authors
  1. Jing Liu
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  2. Zhongwen Guo
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  3. Zhongwei Sun
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  4. Shiyong Liu
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  5. Xupeng Wang
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Corresponding author

Correspondence to Zhongwen Guo .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Jianzhong Li

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Huadong Ma

  3. Tianjin University, Tianjin, Tianjin, China

    Keqiu Li

  4. Chinese Academy of Sciences, Beijing, China

    Li Cui

  5. Chinese Academy of Sciences, Beijing, China

    Limin Sun

  6. Tianjin University, Tianjin, China

    Zenghua Zhao

  7. Tianjin University, Tianjin, China

    Xiaofei Wang

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Liu, J., Guo, Z., Sun, Z., Liu, S., Wang, X. (2018). Online Multi-label Feature Selection on Imbalanced Data Sets. In: Li, J., et al. Wireless Sensor Networks. CWSN 2017. Communications in Computer and Information Science, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-10-8123-1_15

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  • DOI: https://doi.org/10.1007/978-981-10-8123-1_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8122-4

  • Online ISBN: 978-981-10-8123-1

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