Fuzzy support vector machines for biomedical data analysis

Chen, X.; Harrison, Robert W.; Zhang, Y.-Q.

doi:10.1109/grc.2005.1547251

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…For a nonlinearly separable case, it is not possible to satisfy all constrains in (2). Thus, slack variables ξ i , i ∈ {1, 2, .…”

Section: Svmmentioning

confidence: 99%

Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems

Tang

2011

Neural Process Lett

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In dealing with the Two-Class classification problems, the traditional support vector machine (SVM) often cannot achieve good classification accuracy when outliers exist in the training data set. The fuzzy support vector machine (FSVM) can resolve this problem with an appropriate fuzzy membership for each data point. The effect of the outliers can be effectively reduced when the classification problem is solved. In this paper, a new fuzzy membership function is employed in the linear and nonlinear fuzzy support vector machine respectively. The fuzzy membership is calculated based on the structural information of two classes in the input space and in the feature space. This method can distinguish the support vectors and the outliers effectively. Experimental results show that this approach contributes greatly to the reduction of the effect of the outliers and significantly improves the classification accuracy and generalization.

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“…For a nonlinearly separable case, it is not possible to satisfy all constrains in (2). Thus, slack variables ξ i , i ∈ {1, 2, .…”

Section: Svmmentioning

confidence: 99%

Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems

Tang

2011

Neural Process Lett

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“…SVM can easily find optimal solutions on small sample training sets and has excellent generalization ability. Chen et al (2005) proposes a fuzzy system called fuzzy support vector machine (FSVM) to deal with the unreliable generalization ability of SVMs when selected randomly to classify data examples. Margin values from three different SVMs are fuzzified, combining with the accuracy information of each SVM.…”

Section: Related Workmentioning

confidence: 99%

Prediction and analysis of time series data based on granular computing

Yin

2023

Front. Comput. Neurosci.

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The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.

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Fuzzy Support Vector Machine for Genes Expression Data Analysis

Musioł

Wieclawek²,

Mazurek³

Advances in Soft Computing

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Fuzzy support vector machines for biomedical data analysis

Cited by 6 publications

References 7 publications

Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems

Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems

Prediction and analysis of time series data based on granular computing

Fuzzy Support Vector Machine for Genes Expression Data Analysis

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