Abstract: Feature selection is an essential step in the data preprocessing phase in the field of bioinformatics. Traditional feature selection algorithms ignore the problems of dependency relevance and redundancy between features. This paper proposes a joint feature relevance and redundancy (JFRR) algorithm for feature selection. The algorithm uses mutual information to calculate the redundancy values between features and applies joint mutual information to compute the relevance among the set of selected features, candidate features and class labels. Finally, JFRR is validated with the other six feature selection algorithms on two classifiers using nine different gene datasets with classification accuracy metrics (Precision_micro and F1_micro). The experimental results show that the JFRR method can effectively improve classification accuracy.