In order to solve the problem of highly similar data in the condition of small data set expansion, the dimensionality reduced kernel density estimation method is utilized for expanding the small data set, obtaining more accurate expanded data.In addition, in order to solve the problems of low efficiency and weak convergence of CSO, an improved ICSO is proposed to learn the structure:L􀆧vy flight is introduced into the position update formula of rooster to make the algorithm jump further;the dynamic adjustment inertia weight with exponential decline is adopted to hasten local search and augmenting convergence speed; by introducing the most advantageous individual guidance approach, the likelihood of discovering the ideal position is increased.The experimental results show that the proposed algorithm is superior to the MCMC algorithm, the BPSO algorithm, the CSO algorithm, the ADLCSO-I algorithm and the SA-ICSO algorithm in terms of BIC score, accuracy and Hamming distance under conditions of small data set.