针对信息缺失的复杂系统的特征选择

Feature selection for complex information-absent system

特征选择是数据挖掘中的重要研究内容。在现实中,许多待研究系统都很复杂,其中还存在噪声,信息缺失等问题。通过几种特征筛选方法:样本可分类性的评价、对特征集各元素的评价,找出一个信息缺失的复杂系统几个可测特征中对系统性能有较大影响的特征。从而正确指出了系统优化的改进方向,实验结果验证了方法的有效性。

Feature selection is one of important research areas of data mining. In real world, many systems to be investigated are very complicated, some of them have problems of noise and information-absent. Through several feature selection methods - evaluation of samples classification and evaluation of each element of feature set, some features with great influence to the performance of the complex information-absent system can be found. Then a practical and correct direction to improve this system is pointed out. The improvement has been proved by the experimental results.