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Functional Link Artificial Neural Network for Multi-label Classification

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Mining Intelligence and Knowledge Exploration (MIKE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

In this article, a multi-label functional link artificial neural network (MLFLANN) has been developed to efficiently perform multi-label data classification. The input data is functionally expanded to a higher dimension, followed by iterative learning of the multi-label FLANN (MLFLANN) using the training set. The architecture of the network is less complex and the input space dimension is improved in an attempt to overcome the non-linear nature of the multi-label classification problem. The method has been validated on various multi-label datasets and the results are found to be encouraging.

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Author information

Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, India

    Anwesha Law & Ashish Ghosh

  2. Department of Computer Science, Vidyasagar University, Medinipur, 721102, India

    Konika Chakraborty

Authors
  1. Anwesha Law
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  2. Konika Chakraborty
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  3. Ashish Ghosh
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Correspondence to Ashish Ghosh .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Law, A., Chakraborty, K., Ghosh, A. (2017). Functional Link Artificial Neural Network for Multi-label Classification. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71927-6

  • Online ISBN: 978-3-319-71928-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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