首页|New Support Vector Machines Data Have Been Reported by Investigators at Department of Computer Sciences (Handling Multiclass Problem By Intuitionistic Fuzzy Twin Support Vector Machines Based On Relative Density Information)
New Support Vector Machines Data Have Been Reported by Investigators at Department of Computer Sciences (Handling Multiclass Problem By Intuitionistic Fuzzy Twin Support Vector Machines Based On Relative Density Information)
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IEEE
Researchers detail new data in Support Vector Machines. According to news reporting out of Toronto, Canada, by NewsRx editors, research stated, "The intuitionistic fuzzy twin support vector machine (IFTSVM) merges the idea of the intuitionistic fuzzy set (IFS) with the twin support vector machine (TSVM), which can reduce the negative impact of noise and outliers. However, this technique is not suitable for multi-class and high-dimensional feature space problems." Our news journalists obtained a quote from the research from the Department of Computer Sciences, "Furthermore, the computational complexity of IFTSVM is high because it uses the membership and nonmembership functions to build a score function. We propose a new version of IFTSVM by using relative density information. This idea approximates the probability density distribution in multi-dimensional continuous space by computing the K-nearest-neighbor distance of each training sample. Then, we evaluate all the training points by a one-versus-one-versus-rest strategy to construct the k-class classification hyperplanes. A coordinate descent system is utilized to reduce the computational complexity of the training. The boot-strap technique with a 95% confidence interval and Friedman test are conducted to quantify the significance of the performance improvements observed in numerical evaluations." According to the news editors, the research concluded: "Experiments on 24 benchmark datasets demonstrate the proposed method produces promising results as compared with other support vector machine models reported in the literature." This research has been peer-reviewed.
TorontoCanadaNorth and Central AmericaEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesDepartment of Computer Sciences
NETL
NSTL
IEEE
