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

Tao Xie,Jun Yao,Zhiwei Zhou

doi:10.3390/pr7050263

Abstract

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.

Highlights

In the 21st century, cancer is expected to be the major cause of death all over the world
Considering that dragonfly algorithm (DA) has an excellent global search ability and there are few studies on Support vector machine (SVM) classifier with combined kernels in the field of cancer classification, this paper proposed a novel classification algorithm based on DA and SVM classifier with a combined kernel function (DA-CKSVM) to improve the classification ability for cancer diagnosis
10 experiment trials were carried out in each algorithm to evaluate the final results by average classification accuracy and standard deviation, which may minimize the influence of randomness

Summary

Introduction

In the 21st century, cancer is expected to be the major cause of death all over the world. The correct diagnosis of cancer is essential for patients to receive timely and correct treatment. Machine learning plays a unique and important role in the field of cancer treatment. Some researchers applied neural networks to the classification of breast cancer [2,3], and Dongmei Ai et al [4] identified intestinal microorganisms associated with colorectal cancer by means of decision tree aggregation with a random forest model. In the field of cancer diagnosis, many studies have already proven the excellent performance of SVM classifier [9,10,11]. When facing the problem that data cannot be linearly separated, SVM can use the idea of kernel function to map nonlinear features to a high-dimensional space, but it can avoid the problem of “dimensional disaster”

Objectives

Results

Conclusion