Collection selection, ranking collections according to user query is crucial in distributed search. However, few features are used to rank collections in the current collection selection methods, while hundreds of features are exploited to rank web pages in web search. The lack of features affects the efficiency of collection selection in distributed search. In this paper, we exploit some new features and learn to rank collections with them through SVM and RankingSVM respectively. Experimental results show that our features are beneficial to collection selection, and the learned ranking functions outperform the classical CORI algorithm.