Enhanced Supervised Principal Component Analysis for Cancer Classification

Mahdi, Ghadeer Jasim Mohammed; Kalaf, Bayda Atiya; Khaleel, Mundher A.

doi:10.24996/ijs.2021.62.4.28

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…In the previous section, the QP technique was used, but it is very slow. If there are no constraints, gradient descent can be used [16,17]. In general, the gradient goes in the opposite direction to get to the minimum, as shown in Figure 5.a, because the function is in the direction of the steepest slope.…”

Section: Enhanced Stochastic Gradient Descent Svmmentioning

confidence: 99%

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Mahdi,

Seror Faeq Mohammed,

Md Kamrul Hasan Khan

2024

IHJPAS

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Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a very high accuracy and is quite robust. ESGD-SVM is used to analyze the heart disease dataset downloaded from Harvard Dataverse. The entire analysis was performed using the program R version 4.3.

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Section: Enhanced Stochastic Gradient Descent Svmmentioning

confidence: 99%

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Mahdi,

Seror Faeq Mohammed,

Md Kamrul Hasan Khan

2024

IHJPAS

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“…PCA is an unsupervised Feature Reduction method utilized to convert the irrelevant datasets into low dimensional data with minimum reconstruction error (Mahdi et al, 2021;Omuya et al, 2021). PCA is a statistically rigorous method for simplifying data and generating a new collection of variables known as principal components.…”

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

Early & Accurate Forecasting of Mid Term Wind Energy Based on PCA Empowered Supervised Regression Model

Dutta¹,

Shaw²,

Das³

et al. 2021

CIML

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In the Development stage, information about the environment should be accessed. This data is pre-processed with the help of Principal Component Analysis (PCA) to reduce the irrelevant attributes. After that MLA’s such as Decision Tree (DT), Random Forest (RF), KNN, Linear regression (LR) & multilayer Neural Network model (MLP-ANN) models are utilized in testing datasets to predict the wind energy. The Power which is determined necessities to check and separate from the first ability to refresh the framework until and except if the necessary dependability is assembled from learning. In the assessment stage, the prepared expectation framework is then utilized to anticipate the Power for the test samples. In this research four statistical performance indicators & training time used to identify the best-fitted model. In the analysis section, it is seen that PCA Based DT outperformed the others algorithm by means of MAE, MSE, RMSE, Regression & Training time. Keyword : Wind speed prediction, Machine Learning techniques, Regression Analysis, Performance metrics

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Comparison among variable selection models and its application to health dataset

Mseer¹,

Mahdi²

2023

International Conference of Computational Methods in Sciences and Engineering Iccmse 2021

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Enhanced Supervised Principal Component Analysis for Cancer Classification

Cited by 8 publications

References 21 publications

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset

Early & Accurate Forecasting of Mid Term Wind Energy Based on PCA Empowered Supervised Regression Model

Comparison among variable selection models and its application to health dataset

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