sin cosine algorithm

One of the problems with high-dimensional data is choosing the best features, because all the features of the data to find the knowledge that the data lies are not important and vital. For this reason, reducing the size of the data is one of the important issues. Hence in This research has tried a new method using sine cosine algorithm with multiple optimization approach in the Feature selection field. In fact, the innovation of this research is in providing a way to obtain the whole set of appropriate features, which for the first-time sine cosine algorithm has been improved.The proposed method is presented in the wrapper feature selection model and has two steps, which include the feature selection step using the multimodal sine cosine algorithm and the classification step of possible solutions obtained from sine cosine algorithm by the extended nearest neighbor classification method.The proposed method was tested on data sets from uci with different dimensions. The results of the proposed method along with the results of other methods including multimodal optimization and single optimizations are compared and it is observed that the proposed method compared to the single optimization methods, has higher efficiency and compared to multimodal optimization methods, it had better result with a slight difference.In general, the proposed method has been able to reduce the number of features by more than 5% compared to other methods and the average accuracy of the classification compared to the best results of other methods has improved by an average of 2%.