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This paper is concerned with the problems of interaction screening and nonlinear classification in high-dimensional setting.We propose a two-step procedure,ⅡS-SQDA,where in the first step an innovated interaction screening(ⅡS) approach based on transforming the original(S)p(S)-dimensional feature vector is proposed,and in the second step a sparse quadratic discriminant analysis(SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification.