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Classifier Dependent Dimensionality Reduction for Resource Restricted Environments

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Data Science and Analytics (REDSET 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 799))

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Abstract

High dimensionality problems have become prevalent in present day machine learning applications. The voluminous datasets acquired from sources like cameras, spectroscopes, and other sensors need to be analysed and modelled in a way that uses the available computational resources most efficiently. The paper proposes a genetic algorithm optimised neural network model that takes care of the issue mentioned above. A comparison is also drawn between the results produced by the proposed model and those produced by other contemporary dimensionality reduction algorithms.

D. Kalra and C. Dwivedi—Co-first authors.

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Authors and Affiliations

  1. Netaji Subhas Institue of Technology, Dwarka, 110078, New Delhi, India

    Divyanshu Kalra, Chaitanya Dwivedi & Swati Aggarwal

Authors
  1. Divyanshu Kalra
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  2. Chaitanya Dwivedi
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  3. Swati Aggarwal
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Corresponding author

Correspondence to Swati Aggarwal .

Editor information

Editors and Affiliations

  1. University of Arkansas, Fayetteville, AR, USA

    Brajendra Panda

  2. GD Goenka University, Gurugram, Haryana, India

    Sudeep Sharma

  3. GD Goenka University, Gurugram, Haryana, India

    Nihar Ranjan Roy

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Kalra, D., Dwivedi, C., Aggarwal, S. (2018). Classifier Dependent Dimensionality Reduction for Resource Restricted Environments. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_16

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  • DOI: https://doi.org/10.1007/978-981-10-8527-7_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8526-0

  • Online ISBN: 978-981-10-8527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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