Feature selection is a significant task in data mining and machine learning applications which eliminates irrelevant and redundant features and improves learning performance. In many real-world applications, collecting labeled data is difficult, while abundant unlabeled data are easily accessible. This motivates researchers to develop semi-supervised feature selection methods which use both labeled and unlabeled data to evaluate feature relevance. However, till-to-date, there is no comprehensive survey covering the semi supervised feature selection methods. In this paper, semi-supervised feature selection methods are fully investigated and two taxonomies of these methods are presented based on two different perspectives which represent the hierarchical structure of semi-supervised feature selection methods. The first perspective is based on the basic taxonomy of feature selection methods and the second one is based on the taxonomy of semi supervised learning methods. This survey can be helpful for a researcher to obtain a deep background in semi supervised feature selection methods and choose a proper semi-supervised feature selection method based on the hierarchical structure of them.