Feature selection

 

Feature selection 的四个步骤。

Feature selection分为supervised (for classification) 和 unsupervised (for clustering)。根据算法的不同，分为

• the filter model
• the wrapper model
• the hybrid model

区别在于 subset evaluation 的标准不一样，

The filter model relies on general characteristics of the data to evaluate and select feature subsets without involving any mining algorithm. The wrapper model requires one predetermined mining algorithm and uses its performance as the evaluation criterion. It searches for features better suited to the mining algorithm aiming to improve mining performance, but it also tends to be more computationally expensive than the filter model. The hybrid model attempts to take advantage of the two models by exploiting their different evaluation criteria in different search stages.