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Erschienen in:
Does Policy Consistency Affect Economic Growth? Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

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

verfasst von : Vikas Singh, Nishchal K. Verma

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

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Detecting Changes in Time Sequences with the Competitive Detector
Nächstes Kapitel Comparison of Type-2 Fuzzy Integration for Optimized Modular Neural Networks Applied to Human Recognition
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Metadaten
Titel
Deep Learning Architecture for High-Level Feature Generation Using Stacked Auto Encoder for Business Intelligence
verfasst von
Vikas Singh
Nishchal K. Verma
Copyright-Jahr
2018
Buch
Complex Systems: Solutions and Challenges in Economics, Management and Engineering

Print ISBN: 978-3-319-69988-2

Electronic ISBN: 978-3-319-69989-9

Copyright-Jahr: 2018

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