Affiliations: College of Tianfu, SouthWestern University of Finance and Economics, Mianyang, Sichuan, China
Correspondence:
[*]
Corresponding author: Hongyan Xu, College of Tianfu, SouthWestern University of Finance and Economics, Mianyang, Sichuan, China. E-mail:xuhoya@hotmail.com
Abstract: The serial genetic algorithms (SGAs) have been widely
applied in improving support vector machine (SVM) performance (e.g.,
classification accuracy), and these hybrid SGA-SVM methods show good
capability to detect breast cancer. However, there remain two great
challenges: (1) the improvements tend to be at the great cost of
time-consuming training; and (2) the SGA-based search may risk the premature
convergence to local optima and thereby decrease the quality of the
solutions found. The study aimed to investigate the use of parallel genetic
algorithms (PGAs) in improving SVM performance, and build an efficient and
accurate classifier of detecting breast cancer. A coarse-grained parallel
genetic algorithm (CGPGA) was used to select a feature subset and optimize
the parameters of SVM simultaneously. This approach (CGPGA-SVM) was then
applied to a well characterized breast cancer dataset, consisting of 699
samples (458 benign and 241 malignant samples). In addition, the proposed
CGPGA-SVM classier was compared with a range of SVM-based classifiers to
understand its performance improvements. Compared with the SGA-SVM
classifier, the training time of the CGPGA-SVM classier decreased by
75.77% on a commonly used 4-core CPU; moreover, the classification
accuracy and sensitivity of the CGPGA-SVM classifier increased by 0.43%
and 1.25%, respectively.
Keywords: Parallel genetic algorithm, support vector machine, feature selection, parameter optimization, breast cancer diagnosis
