# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 1 Introduction  
With the rapid development of information technology, all walks of life have been generating a large amount of data. Therefore, it is of great significance to promote the development and progress of various industries by mining useful knowledge and information from text data and applying them to information retrieval and data analysis. Text classification is a technology that can effectively organize and manage texts, and facilitate users to quickly obtain useful information, thus becomes an important research direction in the field of information processing. Text classification refers to a text divided into one or multiple predefined categories according to the content of text data, including single-label text classification and multi-label text classification. In single-label text classification, a text is associated with one predefined topic and only divided into one category [1; 2; 3]. However, in multi-label text classification, a text is associated with multiple predefined topics and divided into multiple categories [4; 5; 6]. For example, a news report about a movie may be associated with the topics of \"movie\", \"music\", and others. Also, an article about computers may be associated with the topics of \"computers\", \"software engineering\", and others. In the real world, it is very common for a text to be associated with multiple topics, so it is more practical to study multi-label text classification. Feature selection is an important part of multi-label text classification. Text data is unstructured data, consisting of words. The vector space model is often used to represent text data for facilitating computer processing. In this method, all the terms in all the texts are used as features to constructthe feature space which is called the original feature space. Since a text may consist of thousands of different terms, the original feature space is high-dimensional and sparse. If the original feature space is directly used for text classification, it will be time-consuming and over-fitting. Therefore, reducing the dimension of the original feature space is of significant importance. Feature selection is a commonly used dimensionality reduction technology. It removes useless and redundant features, and only keeps the features with strong category discrimination ability to construct the feature subset, realizing the reduction of feature space dimension and the improvement of classification performance [7; 8]. We study the feature selection method for multi-label text in this paper. Currently, there are some researches on feature selection for multi-label data, which can be divided into three categories: wrapped methods, embedded methods, and filter methods. In wrapped methods, several different feature subsets are constructed in advance, the pros and cons of the feature subsets are then evaluated by the predictive precision of the classification algorithm, and the final feature subset is determined based on the evaluation [9; 10; 11; 12; 13]. In embedded methods, the feature selection process is integrated into the classification model training process, that is, features with high contribution to model training are selected to construct the feature subset in the process of classification model construction [14; 15; 16]. In filter methods, the classification contribution of each feature is calculated as its feature importance, and features with higher importance are selected to construct the feature subset [17; 18; 19; 20]. Based on this selected feature subset, the training of the classification model is performed. Therefore, this type of feature selection methods has low computational complexity and high operational efficiency, and is very suitable for text data [21]. In filter methods, the core is how to calculate the feature importance. At present, the commonly used feature importance calculation method is to count the frequency of features in the whole training set. However, the selected features using this method do not necessarily have strong category discrimination ability.