Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Bourouis, Sami; Alroobaea, Roobaea; Rubaiee, Saeed; Andejany, Murad; Bouguila, Nizar

doi:10.3390/app11135798

Cited by 4 publications

(2 citation statements)

References 68 publications

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“…This flexibility is particularly advantageous in scenarios where the true number of clusters is unknown or varies. This makes Bayesian nonparametric methods well-suited for diverse real-world applications [34,35]. Furthermore, these techniques inherently capture uncertainty in the clustering process, providing a probabilistic assignment of nodes to clusters.…”

Section: Literaturementioning

confidence: 99%

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

Tariq,

Lavangnananda,

Bouvry

et al. 2023

Applied Sciences

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Graph clustering has received considerable attention recently, and its applications are numerous, ranging from the detection of social communities to the clustering of computer networks. It is classified as an NP-class problem, and several algorithms have been proposed with specific objectives. There also exist various quality metrics for evaluating them. Having clusters with the required density can be beneficial because it permits the effective deployment of resources. This study proposes an approach to partitioning and overlapping clustering of undirected unweighted graphs, allowing users to specify the required density of resultant clusters. This required density is achieved by means of ‘Relative Density’. The proposed algorithm adopts an edge-based approach, commencing with the determination of the edge degree for each edge. The main clustering process is then initiated by an edge with an average degree. A cluster is expanded by considering adjacent edges that can be included while monitoring the relative density of the cluster. Eight empirical networks with diverse characteristics are used to validate the proposed algorithm for both partitioning and overlapping clustering. Their results are assessed using an appropriate metric known as the mean relative density deviation coefficient (MRDDC). This is the first work that attempts to carry out partitioning and overlapping graph clustering, which allows user-specified density.

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Section: Literaturementioning

confidence: 99%

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

Tariq,

Lavangnananda,

Bouvry

et al. 2023

Applied Sciences

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show abstract

“…A study proposed a hypothesis that further improving the accuracy of DNN feature selection techniques can be used ( 6 , 7 ). Feature selection is the process of acquiring relevant information and discarding irrelevant ones ( 8 ). The feature selection methods can be either supervised or unsupervised, and the supervised method can be divided into the wrapper, filter or intrinsic methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Arya

Sastry

Motwani

et al. 2022

Front. Public Health

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Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.

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Expectation propagation learning of finite and infinite Gamma mixture models and its applications

Bourouis

Bouguila

2023

Multimed Tools Appl

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Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Cited by 4 publications

References 68 publications

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Expectation propagation learning of finite and infinite Gamma mixture models and its applications

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