The research of Bayesian method from small sample of high-dimensional dataset in poison identification

Wei, Jing Xia; Yu, Hua; Wang, Juyun

doi:10.1109/icsess.2013.6615404

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…An alternative study offers a Bayesian network to select features. The method entails a vast number of features and produces 95.76% accuracy [37]. Cha et al propose a new data description approach, namely support vector data description, which is assessed by employing datasets from the UCI repository.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

Karim¹,

Kaya²,

Güzel

et al. 2020

Sensors

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This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.

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Section: Literature Reviewmentioning

confidence: 99%

“…The proposed framework achieves better outcomes than all studies can be seen in [16,[37][38][39][40][41]55].…”

Section: Spectf Datasetmentioning

confidence: 99%

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

Karim¹,

Kaya²,

Güzel

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

The research of Bayesian method from small sample of high-dimensional dataset in poison identification

Cited by 1 publication

References 12 publications

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification

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