A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset

Mahdi, Ghadeer Jasim Mohammed

doi:10.21123/bsj.2020.17.4.1255

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…The data are mapped to a high-dimensional space in which the discrete superplane can be found in situations when linear separability cannot be achieved. Using a method known as kernel function, the allocation is performed [27,28,29]. In training, 80% of the data was used, and in testing, 20%.…”

Section: Methodsmentioning

confidence: 99%

Determine, Predict and Map Soil pH Level by Fiber Optic Sensor

Al-Sammarraie,

Al-Aani,

Al-Mashhadany

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Soil pH is one of the main factors to consider before undertaking any agricultural operation. Methods for measuring soil pH vary, but all traditional methods require time, effort, and expertise. This study aimed to determine, predict, and map the spatial distribution of soil pH based on data taken from 50 sites using the Kriging geostatistical tool in ArcGIS as a first step. In the second step, the Support Vector Machines (SVM) machine learning algorithm was used to predict the soil pH based on the CIE-L*a*b values taken from the optical fiber sensor. The standard deviation of the soil pH values was 0.42, which indicates a more reliable measurement and the data distribution is normal. The Kriging method gave a prediction accuracy of 65% while the SVM algorithm gave an accuracy of 80%. The root mean square error (RMSE) was 0.36, 0.16 and the mean absolute error (MAE) was 0.37, 0.13, respectively, for the two methods. These two methods allow the prediction of soil pH and thus the assessment of soils, allowing for easier and more efficient management decisions and sustaining productivity.

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Section: Methodsmentioning

confidence: 99%

Determine, Predict and Map Soil pH Level by Fiber Optic Sensor

Al-Sammarraie,

Al-Aani,

Al-Mashhadany

2023

IOP Conf. Ser.: Earth Environ. Sci.

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“…GD-SVM is faster than QP, but it is still slow because computing ∇(𝛽 𝑗 ) takes 𝑝(𝑝) time in complexity, where 𝑝 is the size of the training dataset. If 𝑝 is large, GD-SVM is slow [18,19]. In Stochastic Gradient Descent (SGD), the value of the objective function is improved at each step.…”

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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“…In many cases, it is challenging to sample from a target posterior density. ThenMCMC is used 9 . There are three popular MCMC sampling techniques, such as Metropolis-Hastings, slice sampling 10 , and Gibbs sampling 11 .MCMC methods are derived from a Monte Carlo (MC) 12 .…”

Section: Markov Chain Monte Carlomentioning

confidence: 99%

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

Mahdi,

Salih

2022

Baghdad Sci.J

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Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.

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A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset

Cited by 10 publications

References 15 publications

Determine, Predict and Map Soil pH Level by Fiber Optic Sensor

Determine, Predict and Map Soil pH Level by Fiber Optic Sensor

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

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

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