Text classification results can be hindered when just the bag-of-words model is used for
representing features, because it ignores word order and senses, which can vary with the …

The task of multi-label feature selection (MLFS) is to reduce redundant information and
generate the optimal feature subset from the original multi-label data. A variety of MLFS …

With the rapid growth of medical costs, the control of medical expenses has been becoming
an important task of Health Insurance Department. Traditional medical insurance settlement …

Feature selection is an effective data preprocessing technique that can effectively alleviate
the curse of dimensionality in multi-label learning. The technique selects a subset of features …

Multi-label classification methods aim at assigning more than one label to each instance. In
many real-world classification problems such as image multi-label classification tasks such …

Abstract Hate Speech Detection (HSD) aims to identify whether a text contains hate speech
content, which often refers to discrimination and is even associated with a hate crime. The …

Recently, long short‐term memory (LSTM) networks are extensively utilized for text
classification. Compared to feed‐forward neural networks, it has feedback connections, and …

Abstract Multi-Label Text Classification (MLTC) is one of the most import research of natural
language processing. Although Deep Learning (DL) models have been widely applied to …

We study the literature in major journals and conferences on the usage of shallow learning
and deep learning methods for text classification. Shallow learning techniques such as …

Feature selection, a meaningful preprocessing technique in machine learning, plays a key
role in multi-label learning to select more discriminative features. Recently, multi-label …