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The ever growing presence of data led to a large number of proposed algorithms for classification and especially decision trees over the last years. Recently, it has been shown that decision trees outperform traditional approaches also on limited data. Therefore, increasing the decision tree classification accuracy yields better performance on both huge and moderate sized datasets. This paper proposes a practical algorithm to deal with the data specific classification problem when there are datasets with different properties. Another key motivation of the data specific pruning in the paper is "trading accuracy and size". A novel algorithm called Expert Knowledge Based Pruning, EKBP is proposed to solve this dilemma. We proposed to integrate error rate, missing values and expert judgment as factors for determining data specific pruning for each dataset. In experimental evaluation against three existing techniques on 40 datasets we showed that our best approach outperforms all competitors and yields significant improvement over previous results in terms of accuracy and tree size.