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

Main Article Content

Ghadeer Jasim Mahdi
https://orcid.org/0000-0003-4870-4034

Abstract

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different cancer types is important for cancer diagnosis and drug discovery, SGD-SVM is applied for classifying the most common leukemia cancer type dataset. The results that are gotten using SGD-SVM are much accurate than other results of many studies that used the same leukemia datasets.

Article Details

How to Cite
1.
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset. Baghdad Sci.J [Internet]. 2020 Dec. 1 [cited 2024 Mar. 28];17(4):1255. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4283
Section
article

How to Cite

1.
A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset. Baghdad Sci.J [Internet]. 2020 Dec. 1 [cited 2024 Mar. 28];17(4):1255. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4283

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