Ensemble mutation slime mould algorithm with restart mechanism for feature selection

Existing data acquisition technologies desire further improvement to meet the increasing need for big, accurate, and high-quality data collection. Most of the collected data have redundant information such as noise. To improve the classification accuracy, the dimensionality reduction technique, which is also known as the feature selection, is a necessity for data processing. In this paper, the slime mould algorithm (SMA) is optimized by introducing the composite mutation strategy (CMS) and restart strategy (RS). The improved SMA is named CMSRSSMA, which stands for the CMS, RS, and the improved SMA. The CMS is utilized to increase the population diversity, and the RS is used to avoid the local optimum. In this paper, the CEC2017 benchmark function is used to test the effectiveness of the proposed CMSRSSMA. Then, the CMSRSSMA-SVM model is proposed for feature selection and parameter optimization simultaneously. The performance of the model is tested by 14 data sets from UCI data repository. Experimental results show that the proposed method is superior to other algorithms in terms of classification accuracy, number of features and fitness value on most selected data sets.