Feature selection based on OLCGOA a

Feature selection based on OLCGOA algorithm is run N times on each data set, and k times of cross-validation is performed each time. During the crossover process, data samples are divided into a training set, verification set, and test set in a particular propor- tion. This paper uses K nearest neighbor classifier for classification. At first, the classifier trains and classifies the whole data in the training set, then compares and verifies with the samples in the verification set, and finally runs the selected features in the test data to obtain the computational accuracy