Feature selection algorithm for high dimensional biomedical data classification based on redundant removal

High dimensional biomedical data contain thousands of features, and accurate identification of the main features in these data can be used to classification related data. However, it is usually a large number of irrelevant or redundant features seriously influence classification accuracy. To solve this problem, a new feature selection algorithm based on redundant removal is proposed in this study. Firstly, two redundant criteria are determined by vertical relevance and horizontal relevance. Secondly, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. Finally, to evaluate the effectiveness of our proposed method, contrast experiments based on the classic feature selection algorithm are conducted using (K-nearest neighbour) KNN classifiers, and the results show that our algorithm can effectively improve the classification accuracy.