Alternative splicing is a tissue and developmental stage specific process and greatly increases the biodiversity of proteins. Besides the trans- and cis-factors on the genome level, the process of RNA splicing is also regulated by epigenetic factors. In the present work, we proposed a new method to predict exon skipping events by using the histone methylation and acetylation information. The maximum relevance minimum redundancy method followed by incremental feature selection was performed to select the optimal feature set. Based on the optimized features, our method obtained an overall accuracy of 68.5% in a 10-fold cross validation test for exon skipping event prediction. It is anticipated that our method may become a useful tool for alternative splicing events prediction and the selected optimal features will provide insights into the regulatory mechanisms of epigenetic factors in alternative splicing.