关键词: 观察学习算法, 小样本问题, 概率分布, 贝叶斯学习, 航班备降

Abstract: The Small Sample Size Problem （SSSP） of flights alternate is analyzed and a new prediction model of flights alternate probability distribution based on observational learning is proposed. The model uses relaxation attribute constraints to extract data subset, cubic spline interpolation method to construct the base learning, combines with the virtual data generation strategy to promote the learning agreement. And on this basis, it optimizes the confidence parameters, further improves the prediction model. Experiments of flights alternate data set show that under the large sample, the prediction model of prediction accuracy is higher than the naive Bayesian method and Bayesian network method. And on the small sample data set it analyzes the confidence under different number of alternate flights, to provide decision support for related department.

Key words: observational learning, small sample size problem, probability distribution, Bayes learning, flights alternate