Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

Rustam, Zuherman; Talita, Aini Suri

doi:10.1063/1.4991258

Cited by 22 publications

(17 citation statements)

References 20 publications

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“…Some more experiments also need to be done on a various number of features to see how the algorithm performs over the different number of features. Clustering-based techniques such as [16] may also be applied in attempt to improve the accuracy of classification.…”

Section: Resultsmentioning

confidence: 99%

Application of machine learning on brain cancer multiclass classification

Panca

Rustam²

2017

AIP Conference Proceedings

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Abstract. Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two nonparallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

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Section: Resultsmentioning

confidence: 99%

Application of machine learning on brain cancer multiclass classification

Panca

Rustam²

2017

AIP Conference Proceedings

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“…Membership values are often assigned for fuzzy clustering [16]. Many researchers study the application methods of fuzzy clustering such as Fuzzy Kernel C-Means [17] and Fuzzy K-Medoids [18]. Many researchers have been used fuzzy in many different fields, such as for face recognition [19], linguistics [20], bioinformatics [21], and financial time series that is going to be discussed.…”

Section: Fuzzy Membership Functionmentioning

confidence: 99%

Indonesia Composite Index Prediction using Fuzzy Support Vector Regression with Fisher Score Feature Selection

Rustam¹,

Nurrimah²,

Hidayat³

2019

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

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A precise forecast of stock price indexes may return profit for investors. According to CNNMoney, in the same month as much as 93% of global investors have lost money for trading stock. It is influenced by the stock market itself which is complex, nonlinear, has great noise, and chaotic system. Stock has characteristics that the higher the targeted potential return, the higher possibility of losses. One of the stock price indexes is stock composite index. Exact predictions of stock composite index can be critical for creating powerful market exchanging strategies. There are a lot of research that has been done to assist investors in minimizing possible losses. One of them is by predicting stock composite index. In this paper, a modified supervised learning method used to solve regression problems, Fuzzy Support Vector Regression (FSVR) is focused. As the complexity of many factors influence the movement of stock price prediction, the prediction results of Support Vector Regression (SVR) can not always meet people with precision. Thus, this study implies Fuzzy Support Vector Regression (FSVR) stock prediction model, in which fuzzy membership with mapping function is employed to generate a precise price fluctuation of stock. To assure the use of features on model prediction , Fisher Score is used to find high quality and informative features that can enhance the accuracy. Indonesia Composite Index or Jakarta Composite Index (JKSE) is considered as input data and the result showed that Fisher Score can be applied as feature selection on Indonesia Composite Index prediction with the best model is eleven out of fifteen features with 80% of training data with 0.043529 error.

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“…Fuzzy c-means and fuzzy kernel c-means have been applied in various field, not only for cancer or to classify disease. Application of fuzzy c-means and fuzzy kernel cmeans has been used in example for predicting the direction of Indonesian stock price movement [12], and to predict the composite index price [13], and also for forecasting stock market momentum [14], and to solve intrusion data system (IDS) that they claim provides better result [15].…”

Section: Introductionmentioning

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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Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

Cited by 22 publications

References 20 publications

Application of machine learning on brain cancer multiclass classification

Application of machine learning on brain cancer multiclass classification

Indonesia Composite Index Prediction using Fuzzy Support Vector Regression with Fisher Score Feature Selection

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

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