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Feature Reduction for Multi Label Classification of Discrete Data

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

We describe a novel multi-label classification algorithm which works for discrete data. A matrix which gives the membership value of each discrete value of each attribute for every class. For a test pattern, looking at the values taken by each attribute, we find the subset of classes to which the pattern belongs. If the number of classes are large or the number of features are large, the space and time complexity of this algorithm will go up. To mitigate this problems, we have carried out feature selection before carrying out classification. We have compared two feature reduction techniques for getting good results. The results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results. Using feature reduction our classification accuracy and running time for algorithm is improved. The performance of the above algorithm is evaluated using some benchmark datasets and the results have been compared with the algorithm multi-label KNN or ML-KNN and found to give good results.

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Authors and Affiliations

  1. Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560012, India

    V. Susheela Devi & Bhupesh Akhand

Authors
  1. V. Susheela Devi
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  2. Bhupesh Akhand
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Corresponding author

Correspondence to V. Susheela Devi .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Susheela Devi, V., Akhand, B. (2016). Feature Reduction for Multi Label Classification of Discrete Data. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_49

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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