GERMAN CREDIT RISKS CLASSIFICATION USING SUPPORT VECTOR MACHINES

Öz

Support Vector Machines (SVM) is one of the most popular classification algorithms. SVM penalty parameter and the kernel parameters have high impact over the classification performance and the complexity of the algorithm. So, this brings the problem of choosing the suitable values for SVM parameters. This problem can be solved using meta-heuristic optimization algorithms. Salp Swarm Algorithm (SSA) and Crow Search Algorithm (CSA) are new meta-heuristic algorithms. SSA is a swarm algorithm that is inspired from a mechanism salps forming in deep ocean called salp chain. CSA algorithm is inspired by the intelligent behavior of crows. In this paper, SVM parameter optimization is done using SSA and CSA. German Credit dataset from the UCI data repository is used for the experiments. All experiments results are gathered from a 10-fold cross validation block. Evaluation criteria determined as accuracy, sensitivity, specificity and AUC. SSA and CSA gave accuracy results of 0.72±4.62 and 0.71±3.53 respectively. Also, ROC curves and box plots of the algorithms are given. CSA algorithm draws better graphs.