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Deep Learning Architecture for High-Level Feature Generation Using Stacked Auto Encoder for Business Intelligence

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Complex Systems: Solutions and Challenges in Economics, Management and Engineering

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 125))

Abstract

In the era of modern world, faster development and wider use of digital technology generates large amount of data in digital space. Handling such large amount of data by conventional machine learning algorithms is difficult because of heterogeneous nature and large size of data. Deep learning strategy, is an advancement in machine learning research to deal with such heterogeneous nature and large size of data and extract high-level representations of data through a hierarchical learning process. This paper proposes novel multi-layer feature selection with conjunction of Stacked Auto-Encoder (SAE) to extract high level features or representations and eliminate the lower level features or representations from data. The proposed approach is validated on the Farm Ads dataset and the result is compared with various conventional machine learning algorithms. The proposed approach has outperformed as compared to conventional machine learning algorithms for the given dataset.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Vikas Singh & Nishchal K. Verma

Authors
  1. Vikas Singh
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  2. Nishchal K. Verma
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Corresponding author

Correspondence to Nishchal K. Verma .

Editor information

Editors and Affiliations

  1. Lyon Neurosciences Research Center and Biomechanical and Impacts Laboratory, University Lyon 1, Villeurbanne, France

    Christian Berger-Vachon

  2. Department of Economics and Business Organization, University of Barcelona, Barcelona, Spain

    Anna María Gil Lafuente

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Intelligent Information Systems, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

    Yuriy Kondratenko

  5. Department of Management Control and Information Systems, University of Chile, Santiago, Chile

    José M. Merigó

  6. Department of Civil, Energy, Environment and Materials Engineering, Mediterranean University of Reggio Calabria, Reggio Calabria, Italy

    Carlo Francesco Morabito

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Singh, V., Verma, N.K. (2018). Deep Learning Architecture for High-Level Feature Generation Using Stacked Auto Encoder for Business Intelligence. In: Berger-Vachon, C., Gil Lafuente, A., Kacprzyk, J., Kondratenko, Y., Merigó, J., Morabito, C. (eds) Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Studies in Systems, Decision and Control, vol 125. Springer, Cham. https://doi.org/10.1007/978-3-319-69989-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-69989-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69988-2

  • Online ISBN: 978-3-319-69989-9

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