Abstract: This paper deals with the elastic property prediction of inorganic materials by using three feature selection methods ( Filter，RFE and LASSO) and three machine learning algorithms ( linear regression，ridge regression and support vector regression) ． In the investigation，first，the best feature subset is selected to predict the elastic properties of inorganic compounds from a large number of multi-scale feature sets． Next，the most effective model that combines both feature selection and machine learning is identified for predicting the elastic properties of materials． Then，the performances of different combinations of feature selection methods and machine learning models are compared by anlyzing the feature subset obtained via different feature selection methods． Experimental results indicate that ( 1) Filter + SVR method helps to achieve the best prediction performance; ( 2) the machine learning model has greater influence on the prediction results than the feature selection method; and ( 3) the feature subset selected by the feature selection method mainly includes the material characteristics such as melting point，crystal structure and Mendeleev number． This research provides a way to finding predictive descriptors for elastic property of inorganic compounds and developing more effective prediction methods．

Key words: materials informatics, feature selection, machine learning, elastic property prediction