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Multi-class Semi-supervised Logistic I-RELIEF Feature Selection Based on Nearest Neighbor

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Advances in Knowledge Discovery and Data Mining (PAKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11440))

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Abstract

The multi-class semi-supervised logistic I-RELEIF (MSLIR) algorithm has been proposed and showed its feature selection ability using both labeled and unlabeled samples. Unfortunately, MSLIR is poor when predicting labels for unlabeled samples. To solve this issue, this paper presents a novel multi-class semi-supervised logistic I-RELEIF based on nearest neighbor (MSLIR-NN) for multi-class feature selection tasks. To generate better margin vectors for unlabeled samples, MSLIR-NN uses the nearest neighbor scheme to first predict the labels of unlabeled samples and then calculates their margin vectors according to these estimated labels. Experimental results demonstrate that MSLIR-NN can improve the prediction accuracy of unlabeled data.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Soochow Scholar Project of Soochow University, and by the Six Talent Peak Project of Jiangsu Province of China.

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Authors and Affiliations

  1. School of Computer Science and Technology, Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Baige Tang & Li Zhang

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang

Authors
  1. Baige Tang
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  2. Li Zhang
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Corresponding author

Correspondence to Li Zhang .

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Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Tang, B., Zhang, L. (2019). Multi-class Semi-supervised Logistic I-RELIEF Feature Selection Based on Nearest Neighbor. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_22

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  • DOI: https://doi.org/10.1007/978-3-030-16145-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16144-6

  • Online ISBN: 978-3-030-16145-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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