A Study of a Support Vector Machine Algorithm with an Orthogonal Legendre Kernel According to Neutrosophic logic and Inverse Lagrangian Interpolation

Mohammed Alshikho, Maissam, S. Broumi
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引用次数: 7

Abstract

The decision-making process is greatly affected by the data collection stage. If the data collection process is not well controlled, i.e. there is some data lost due to the poor quality of the devices used or the lack of accuracy in the data entry process...etc., this will affect the work of the SVM algorithm, which is considered one of the best. Most of the workbooks suffer from the problems of missing and anomalous data. In this paper, we propose a method to treat the missing and anomalous data by reshaping the data set defined by the classical method into the neutrosophical data set by calculating the amount of true T, false F, and neutrality I in the neutrosophical set using inverse Lagrangian interpolation. We noticed the superiority of our proposed method for processing missing data over the method of [21], then we trained a support vector machine algorithm with orthogonal legender kernel on a breast cancer dataset taken from the Statistics Department of Al-Bayrouni Hospital in Damascus, where the proposed algorithm achieved a classification accuracy of 97%. The reason we chose a support vector machine classifier with an orthogonal legender kernel has two goals: the first is to eliminate the repetition of support vectors in the feature space. The second is to solve the problem of non-linear data distribution.
基于中性逻辑和逆拉格朗日插值的正交勒让德核支持向量机算法研究
决策过程受数据收集阶段的影响很大。如果数据收集过程没有得到很好的控制,即由于使用的设备质量差或数据输入过程缺乏准确性而导致一些数据丢失……,这将影响SVM算法的工作,该算法被认为是最好的算法之一。大多数练习册都存在数据缺失和数据异常的问题。在本文中,我们提出了一种处理缺失和异常数据的方法,通过使用逆拉格朗日插值计算中性数据集中的真T,假F和中性I的数量,将经典方法定义的数据集重塑为中性数据集。我们注意到我们提出的方法在处理缺失数据方面优于[21]的方法,然后我们在大马士革al - bayroui医院统计部门的乳腺癌数据集上训练了具有正交legender核的支持向量机算法,其中提出的算法实现了97%的分类准确率。我们选择具有正交图例核的支持向量机分类器的原因有两个目标:第一个目标是消除特征空间中支持向量的重复。二是解决非线性数据分布问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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