Identification of protein interaction sites has significant impact on understanding protein function, elucidating signal transduction networks and drug design studies. With the exponentially growing protein sequence data, predictive methods using sequence information only for protein interaction site prediction have drawn increasing interest. In this article, we propose a predictive model for identifying protein interaction sites. Without using any structure data, the proposed method extracts a wide range of features from protein sequences. A random forest-based integrative model is developed to effectively utilize these features and to deal with the imbalanced data classification problem commonly encountered in binding site predictions.