Abstract: In the field of machine learning,the problem of intrinsic biases in models has received increasing attention,and these biases often originate from imbalances in the training data or flaws in the algorithm design,which lead to unfair treatment of certain groups in the prediction results.To address this problem,this paper proposes a fairness-enhanced decision tree algorithm,which effectively reduces the imbalance in the data by introducing a fairness preprocessing method,and changes the traditional decision tree splitting criterion by integrating classification accuracy and fairness in the splitting criterion of the decision tree.The proposed method aims to achieve the fair distribution of prediction results among different groups,reduce the bias in model decision-making,and ensure that all individuals are treated fairly.Experimental results show that the proposed method demonstrates good performance under multiple fairness metrics,significantly reduces the prediction bias among different groups,and exhibits stronger fairness bias-correction performance than the existing traditional algorithms.

Key words: Machine learning, Classification, Decision tree, Fairness, Preprocessing