Comparison between Support Vector Machine and Fuzzy C-Means as Classifier for Intrusion Detection System

Rustam, Zuherman; Zahras, Durrabida

doi:10.1088/1742-6596/1028/1/012227

Cited by 22 publications

(14 citation statements)

References 4 publications

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“…In this research, we used Fuzzy C-Means (FCM) and the algorithm can be seen in Fig. 1 [17]. Fuzzy C-Means classification's accuracy is dependent on the types of data.…”

Section: A Fuzzy C-means (Fcm) Methodsmentioning

confidence: 99%

Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data

Rustam

Kamalia

Hidayat

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Among the inherited blood disorders in Southeast Asia, thalassemia is the most prevalent. Thalassemias are pathologies that derive from genetic defects of the globin genes. Thalassemia is also considered a health burden among the world's population. Thalassemia cannot be cured, but there is a method to prevent the occurrence of thalassemia by early detection with screening. The aim is to identify the suspected unrecognised diseases in a population that seems healthy and asymptomatic using tests, examinations, or other procedures that can be applied quickly and easily to the target population. Research on thalassemia has been done extensively, such as testing the accuracy of β-thalassemia data in Thailand using the Bayesian Network and Multinomial Logistic Regression. In this study, we will compare the performance of the classification of thalassemia data by Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means. The author uses thalassemia data from Indonesia, acquired from Harapan Kita Children and Womens's Hospital, Jakarta, that consists of 82 thalassemia samples from the patients of thalassemia and 68 nonthalassemia samples with 11 features. In total, there are 150 data patients used in this paper. The results show the accuracy of the classification. The accuracy of FCM is 100% when training data is 90%, FRCM is 100% when training data is 90%, and FKRCM, which is the modified Fuzzy, 100% when we use the = 0.0001 and 80% & 90% training data. This result denote that Fuzzy C-Means, Fuzzy Robust C-Means, and Fuzzy Kernel Robust C-Means perfectly classify thalassemia data from Indonesia.

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“…In this research, we used Fuzzy C-Means (FCM) and the algorithm can be seen in Fig. 1 [17]. Fuzzy C-Means classification's accuracy is dependent on the types of data.…”

Section: A Fuzzy C-means (Fcm) Methodsmentioning

confidence: 99%

Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data

Rustam

Kamalia

Hidayat

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In other words, this method brings the form of mapping input into a space that has higher dimension to support nonlinear classification problems where the hyperplane causes the maximum separation between each class [24]. Therefore, the optimization problem observed is defined to minimize the following function [25] while determining the value of weight w ∈ ℝ n and the bias b ∈ ℝ n .…”

Section: Support Vector Machinesmentioning

confidence: 99%

The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Hartini

Rustam

2020

TELKOMNIKA

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Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.

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“…In 1981, Jim Bezdek introduced Fuzzy C-Means, which is a clustering data technique where each data point in a group is determined by its degree of membership. The basic concept of the FCM is to determine the center of the cluster that will mark the average location of each cluster [8]. Each data point in each cluster has a degree of membership.…”

Section: A Fuzzy C-means Clusteringmentioning

confidence: 99%

“…However, in the initial conditions, the cluster center and the degree of membership are not accurate. Therefore, the center of the cluster and the degree of membership are corrected repeatedly to ensure they are in the right location [8]. The output of the FCM method is not a fuzzy inference system, but the degree of the cluster center and the degree of membership for each data.…”

Section: A Fuzzy C-means Clusteringmentioning

confidence: 99%

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Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification

Rustam¹,

Pandelaki²,

Utami³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Osteoarthritis is a chronic joint disease that occurs when the protective cartilage that cushions the ends of bones wears down over time and fails to be repaired. The common form of the disease is knee osteoarthritis while it can affect all body parts with joints, such as hands, ankles, hips, and spine. The major cause of knee osteoarthritis is the continuous depletion of its cartilage. During the diagnosis, machine learning is used because early prevention is necessary for proper treatment. This study, therefore, considers classification methods of Support Vector Machine (SVM) and clustering methods using fuzzy clusterings such as Fuzzy C-Means (FCM), Fuzzy Possibilistic C-Means (FPCM), and Fuzzy Possibilistic C-Means based on kernel (FPCMK) to analyze of knee osteoarthritis. SVM is a machine learning technique that works based on the principle of structural risk minimization (SRM) to obtain the best hyperplane to separate two or more classes in input space. Otherwise, the fuzzy clustering is to determine the value of a distance and to know and measure the similarity of each object to be observed. FPCMK uses the kernel Radial Base Function (RBF) in the fuzzy clustering method. The kernel function is applicable for handling non-separable data problems. This method will be compared to the level of the measured parameter; their accuracy, recall, precision, and f1 score. The greatest level of accuracy is generated from SVM with an accuracy value of 86.7%, then followed by FPCMK with an accuracy value of 85.5%.

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Comparison between Support Vector Machine and Fuzzy C-Means as Classifier for Intrusion Detection System

Cited by 22 publications

References 4 publications

Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data

Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data

The comparison study of kernel KC-means and support vector machines for classifying schizophrenia

Comparison Support Vector Machine and Fuzzy Possibilistic C-Means based on the kernel for Knee Osteoarthritis data Classification

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