示例学习与特征选择的规划模型方法

以扩张矩阵理论为基础，应用数学规划理论提出了一种规划模型求解方法，可以更好地实现概念学习和特征提取。与传统的启发式算法相比，采用遗传算法求解的规划模型可以找到多个全局最优解以及可行解。实例计算表明了该方法的有效性。

Integer-programming model for learning from examples and feature subset selection based on extension matrix

Learning from examples and feature subset selection are the two basic problems and also the bottle neck in concepts extraction of machine learning. Based on extension matrix formed on positive and negative examples, we set up the integer programming model (IPM) for optimal rule extraction and feature subset selection. The IPM method can find the multiple optimal solution in theory and practice, and experiments reveal that IPM , while solved by genetic algorithms, runs more efficiently compared with heuristic algorithms as FCV, GS, HCV, GFS, etc.. We have used IPM as the core method of knowledge acquisition in data mining and knowledge discovery in databases about financial analysis and tax auditing, and got effective results.