Improving classification performance of cancer microarray data using hybridization of binary grey wolf and particle swarm optimization
Date
2019-10-11
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Abstract
In this study, we have proposed hybridization of binary grey wolf Optimization and particle swarm
optimization (BGWOPSO) method and we compared this hybrid optimization method with
Particle Swarm Optimization (PSO) and Binary Grey Wolf Optimization (BGWO). We have used
five significantly different classifier such as K-nearest Neighbor (KNN), Support Vector Machine
(SVM), Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF).
Furthermore, we have used ratio comparison validation for the 10-folds cross-validation method
for feature selection methods. Data sets such as Leukemia, Breast Cancer, and Liver Cancer are
used to apply the combinations and measure accuracy as well as the area under ROC. Moreover,
the results show that Hybrid optimization method (BWOPSO), significantly outperformed the both
binary grey wolf optimization (BGWO) and particle swarm optimization (PSO) method, when
using several performance measures including accuracy, selecting the best optimal features.
Secondly, combinations of classifier and feature pre-processing method significantly improve the
accuracy. Lastly, the AUC value is been displayed in this study.
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Keywords
Hybrid binary optimization, Grey wolf optimization, Particle swarm optimization, Feature, classification.