A Review on Multilevel wrApper Verification System with maintenance Model Enhancement

Patil, Nigonda M.; Khandagale, H. P.

doi:10.22161/ijaers/3.12.11

“…To manage the dif�iculties produced by high-dimensional datasets, dimensionality reduction methods like principal component analysis (PCA) and independent component analysis (ICA) have been utilised [20,21]. ICA, applying higher order statistics, isolates independent components with more information than PCA, minimising data dimensionality while boosting classi�ier performance and convergence time [13,22,23].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques

CHARY

2023

Preprint

0

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In this work, we will evaluate the impact of independent component analysis (ICA) on a breast cancer decision support system's feature reduction capabilities. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset will be utilised to construct a one-dimensional feature vector (IC). We will study the performance of k-NN, ANN, RBFNN, and SVM classifiers in spotting mistakes using the original 30 features. Additionally, we will compare the IC-recommended classification with the original feature set using multiple validation and division approaches. The classifiers will be tested based on specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and receiver operating characteristic (ROC) curve. This effort attempts to boost the medical decision support system's efficiency while minimising computational complexity.

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Evaluation of relationship of hardness and Larson Miller parameter on various types of heat resistant steels for prediction of creep rupture time application

Anwar¹,

Mabruri²,

Mochtar³

et al. 2022

Special Issue of the 4th International Symposium on Advanced Materials and Nanotechnology (iSAMN 2020)

0

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Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques

CHARY

2023

Preprint

0

View full text Add to dashboard Cite

In this work, we will evaluate the impact of independent component analysis (ICA) on a breast cancer decision support system's feature reduction capabilities. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset will be utilised to construct a one-dimensional feature vector (IC). We will study the performance of k-NN, ANN, RBFNN, and SVM classifiers in spotting mistakes using the original 30 features. Additionally, we will compare the IC-recommended classification with the original feature set using multiple validation and division approaches. The classifiers will be tested based on specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and receiver operating characteristic (ROC) curve. This effort attempts to boost the medical decision support system's efficiency while minimising computational complexity.

show abstract

A Review on Multilevel wrApper Verification System with maintenance Model Enhancement

Abstract: Abstract-

Cited by 3 publications

References 2 publications

Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques

Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques

Evaluation of relationship of hardness and Larson Miller parameter on various types of heat resistant steels for prediction of creep rupture time application

Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques

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