A Parallel PCA Neural Network Approach for Feature Extraction

Oliveira, Patrícia Rufino; Digiampietri, Luciano Antônio; Honda, Willian Y.; Pereira, Marcone C.

doi:10.21528/cbic2011-39.6

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Principal Component Analysis With Neural Network (Pca-nn)1

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2021

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“…PCA needed 47 components to capture 95 percent of the variance in Extension of Alizadeh Sani. The PCA-Neural Network uses an unsupervised learning process that is based on variations of the Neural Network rule [30].…”

Section: Principal Component Analysis With Neural Network (Pca-nn)mentioning

confidence: 99%

A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms

Verma¹,

Awasthi²,

Sahu³

2021

RIA

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Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.

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Section: Principal Component Analysis With Neural Network (Pca-nn)mentioning

confidence: 99%