In this paper, a post-pruning method known as zero-one loss function pruning (ZOLFP) that is based on zero-one loss function is introduced. The proposed ZOLFP method minimizes the expected loss, rather than evaluating the misclassification error rate of a node and its subtree. The subtree is pruned when expected loss of the node is less than or equal to the sum of the loss of its leaves. The experimental results demonstrate that ZOLFP method outperforms. Un-pruned C4.5 Decision Tree (UDT-C4.5) algorithm, reduced error pruning (REP), and minimum error pruning (MEP) in terms of performance accuracy in all used datasets. It is also shown that the complexity of the proposed method ZOLFP is not more than the complexity of REP and MEP methods. Furthermore, the results show that ZOLFP method achieves satisfactory results compared to REP, MEP, and UDT-C4.5 algorithms in terms of precision score, recall score, true positive rate, false positive rate, F-measure, and area under ROC scores during the experiment process.