Deep Bayesian network architecture for Big Data mining. (15th January 2018)

• Record Type: Journal Article
• Authors: Njah, Hasna; Jamoussi, Salma; Mahdi, Walid
• Other Names: Sahuquillo Jesús Escudero guestEditor.; Garcia Pedro Javier guestEditor.; Bellatreche Ladjel guestEditor.; Leung Carson guestEditor.; Xia Yinglong guestEditor.; Baz Didier El guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-01-15
• Subjects: Bayesian network -- Big Data -- classification -- clustering -- Deep Learning -- latent variable; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4418
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Classical Datamining methods are facing various challenges in the era of Big Data. Between the need of fast knowledge extraction and the high flows of data acquired in small slots of time, these methods became shifted. The variability and the veracity of the Big Data perplex the Machine Learning process. The high volume of Big Data yields to a congested learning because the classic methods are designed for small sets of features. Deep Learning has recently emerged in the aim of handling voluminous data. The concept of the Deep induces the conversion of the features into a new abstracted representation in order to optimize an objective. Although the Deep Learning methods are experimentally promising, their parameterization is exhaustive and empirical. To tackle these problems, we utilize the causality and the uncertainty of the Bayesian Network in order to propose a new Deep Bayesian Network architecture. We provide a new learning algorithm for this multi‐layered Bayesian Network with latent variables. We evaluate the proposed architecture and learning algorithms over benchmark datasets. We used high‐dimensional data in order to simulate the Big Data challenges, which are imposed by the volume and veracity aspects. We demonstrate the effectiveness of our contribution under these constraints.