Semi-hierarchical naïve Bayes classifier

Njah, Hasna; Jamoussi, Salma; Mahdi, Walid

doi:10.1109/ijcnn.2016.7727414

Cited by 5 publications

(12 citation statements)

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“…Furthermore, the variability of the hierarchical structure of the HBN depends on the goal of the application. [36][37][38] They showed an improved performance as compared to the classical Bayesian classifiers. The strict and loose HBNs were adopted for the modeling of variables and for the supervised classification of the instances.…”

Section: Hierarchical Bayesian Networkmentioning

confidence: 99%

“…In this context, a variety of Bayesian classifiers that follow the HBN structure were proposed. [36][37][38] They showed an improved performance as compared to the classical Bayesian classifiers.…”

Section: Hierarchical Bayesian Networkmentioning

confidence: 99%

“…36 Overlapped cliques can be presented through the application of this method. Therefore, it is imperative to define the highly connected features in the observed high-dimensional dataset.…”

Section: Features Clusteringmentioning

confidence: 99%

“…Inspired by principle that was used in the work of Njah et al, 36 we propose a new algorithm for computing the parameters of Y + 1 given U and the optimal cardinality k * . Inspired by principle that was used in the work of Njah et al, 36 we propose a new algorithm for computing the parameters of Y + 1 given U and the optimal cardinality k * .…”

Section: Latent Variable Learningmentioning

confidence: 99%

“…We apply a graph-based clustering algorithm. 36 Overlapped cliques can be presented through the application of this method. This overlapping has an important role for modeling the complex relations between the variables.…”

Section: Features Clusteringmentioning

confidence: 99%

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Deep Bayesian network architecture for Big Data mining

Njah

Jamoussi

Mahdi

2018

Concurrency and Computation

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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.

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Section: Hierarchical Bayesian Networkmentioning

confidence: 99%

Section: Hierarchical Bayesian Networkmentioning

confidence: 99%

Section: Features Clusteringmentioning

confidence: 99%

Section: Latent Variable Learningmentioning

confidence: 99%

Section: Features Clusteringmentioning

confidence: 99%

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