scholarly journals Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis

2019 ◽  
Vol 92 ◽  
pp. 103124 ◽  
Author(s):  
Dandan Zhao ◽  
Hong Liu ◽  
Yuanjie Zheng ◽  
Yanlin He ◽  
Dianjie Lu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Support Vector Machine ◽  
Kernel Function ◽  
Cancer Diagnosis ◽  
Support Vector

scholarly journals Twin support vector machine using kernel function for colorectal cancer detection

2021 ◽  
Vol 10 (6) ◽  
pp. 3121-3126
Author(s):  
Zuherman Rustam ◽  
Fildzah Zhafarina ◽  
Jane Eva Aurelia ◽  
Yasirly Amalia
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Kernel Function ◽  
Cancer Detection ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Medical Field ◽  
Running Time ◽  
Polynomial Kernels

Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.

scholarly journals DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 263 ◽  
Author(s):  
Tao Xie ◽  
Jun Yao ◽  
Zhiwei Zhou
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Cancer Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Dragonfly Algorithm ◽  
Cancer Data

As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

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