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A Novel Hybrid Technique for Big Data Classification Using Decision Tree Learning

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Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 775))

Abstract

Machine learning techniques have proven to be very effective in classification scenarios. However, learning approaches of these algorithms remain unsuccessful to scale up to massive sized data. To handle and classify voluminous big data efficiently, a hybrid algorithm of logical combination of machine learning technique decision tree along with big data analytical platform Hive is introduced in this paper. The objective of proposed solution is to uplift the efficiency of traditional decision tree learning classifier by using map reduce based framework. Experiments designed on big dataset using proposed hybrid approach have shown promising results in terms of improved accuracy.

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Author information

Authors and Affiliations

  1. University School of Information Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

    Khyati Ahlawat & Amit Prakash Singh

Authors
  1. Khyati Ahlawat
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  2. Amit Prakash Singh
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Corresponding author

Correspondence to Khyati Ahlawat .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Ahlawat, K., Singh, A.P. (2017). A Novel Hybrid Technique for Big Data Classification Using Decision Tree Learning. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_10

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  • DOI: https://doi.org/10.1007/978-981-10-6427-2_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6426-5

  • Online ISBN: 978-981-10-6427-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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