基于filter-wrapper的两步特征变量提取方法

特征变量选择是高维数据分类问题的核心,主要有过滤法和缠绕法两种特征变量选择方法。针对"过滤法与分类算法相互独立,不利于对分类性能优化,而缠绕法依赖于分类算法,在高维高噪的数据中容易过拟合"这个问题,为了能有效提取特征变量,提出了一种新的特征提取方法,即filter-wrapper两步法,先通过有监督奇异值分解方法降维去噪,粗选出一部分备选变量;再应用MonteCarlo决策树策略从备选变量中精选出重要的特征变量。通过以典型的高维高噪数据为例验证了该方法,实验结果表明了上述方法的可行性和有效性。

Two-step feature selection algorithm based on filter and wrapper

Feature selection is an essential step to classification with high dimensional data.Filter method is independent of classifiers,and hence the classification quality irrelevant,wrapper method is a classification supervised method,which is prone to overfitting in high dimensional and high noise data.Aiming at these problems of filter and wrapper methods,a new feature selection approach was proposed,which combined filter and wrapper methods,first supervised singular value decomposition was used to reduce varia...