# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 2 Related Works  
The purpose of feature selection is to reduce the dimension of the feature space and improve the efficiency and performance of the classification through removing irrelevant and redundant features. The filter feature selection methods are viewed as a pure pre-processing tool and have low computational complexity. As a result, there are many scholars focusing on the research of this type of methods. For multi-label data, there are two main research ideas in filter feature selection methods.  
One method is the feature selection method based on the idea of algorithm adaptation. In this type of methods, the feature importance calculation methods commonly used in single-label feature selection are adapted to be suitable for multi-label data, then the feature selection for multi-label data is performed on them [18; 19; 20; 26; 27]. [PERSON] et al. [18] proposed a filter multi-label feature selection method by adapting mutual information. This method was evaluated on multi-label data sets of various fields, including text. [PERSON] et al. [20] proposed a method named max-dependency and min-redundancy, which considers two factors of multi-label feature, feature dependency, and feature redundancy. Three text data sets, Artificial, Health, and Recreation were used to evaluate the method. [PERSON] et al. [26] extended the technique fast correlation-based filter [28] to deal with multi-label data directly. Text data sets were also used in his experiments. In this type of methods, multi-label data is directly processed for feature selection, so the computational complexity is very high when there are many categories in the data set.  
The other is the feature selection method based on the idea of problem transformation. In this type of methods, multi-label data is transformed into single-label data, then the feature selection is performed on single-label data [17; 29; 30; 31; 32; 33; 34]. Since a piece of multi-label data belongs to multiple categories, the single-label feature importance calculation methods can't directly deal with it. A simple transformation technique is used to convert a piece of multi-label data into a single-label one, selecting just one label for each sample from its multi-label subset. This label can be the most frequent label in the data set (select-max), the least frequent label (select-min), or a random label (select-random). [PERSON] et al. [17] designed a transformation method based on the definition of ranking loss for multi-label feature selection and tested it on four text data sets. [PERSON] et al. [29] proposed a transformation method based on entropy and made the application of traditional feature selection techniques to the multi-label text classification problem. [PERSON] et al. [31] proposed four multi-label feature selection methods by combining two transformation methods and two feature importance calculation methods, and verified them on data sets of various fields, including the field of text. [PERSON] et al. [34] focused on the feature importance calculation method based on mutual information and presented a multi-label feature selection method. Five text data sets were used to evaluate the proposed method. In this type of methods, feature selection is performed on single-label data, which reduces the complexity of feature importance calculation and is very suitable for text feature space. The transformation technique and feature importance calculation method are the two key technologies in this type of multi-label feature selection methods. As a result, the label assignment methods, which are a type of commonly used transformation techniques, and some feature importance calculation methods are briefly introduced in the following.