Gene selection using gaussian kernel support vector machine based recursive feature elimination with adaptive kernel width strategy

摘要: Recursive feature elimination based on non-linear kernel support vector machine (SVM-RFE) with parameter selection by genetic algorithm is an effective to perform gene and cancer classification in some degree, but its calculating complexity too high for implementation. In this paper, we propose a new strategy use adaptive parameters the recursive implemented Gaussian SVMs as better alternatives aforementioned pragmatic reasons. The proposed method performs well selecting genes achieves accuracies these two datasets