Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study

Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classification tasks. However, its behavior strongly depends on some parameters, making tuning these parameters a sensitive step to maintain a good performance. On the other hand, and as any other classifier, the performance of SVM is also affected by the input set of features used to build the learning model, which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets. In this paper, the MRFO + SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously. The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets. Additionally, it is applied to a disease Covid-19 dataset. The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters, and its acceptable performance to deal with feature selection problem.