# Multilabel Text Classification with Label-Dependent Representation
###### Abstract  
Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computational methods. Text representation for classification purposes has traditionally been performed using a vector space model due to its good performance and simplicity. Moreover, the classification of texts via multilabeling has typically been approached by using simple label classification methods, which require the transformation of the problem studied to apply binary techniques, or by adapting binary algorithms. Over the previous decade, text classification has been extended using deep learning models. Compared to traditional machine learning methods, deep learning avoids rule design and feature selection by humans, and automatically provides semantically meaningful representations for text analysis. However, deep learning-based text classification is data-intensive and computationally complex. Interest in deep learning models does not rule out techniques and models based on shallow learning. This situation is true when the set of training cases is smaller, and when the set of features is small. White box approaches have advantages over black box approaches, where the feasibility of working with relatively small sets of data and the interpretability of the results stand out. This research evaluates a weighting function of the words in texts to modify the representation of the texts during multilabel classification, using a combination of two approaches: problem transformation and model adaptation. This weighting function was tested in 10 referential textual data sets, and compared with alternative techniques based on three performance measures: Hamming Loss, Accuracy, and macro-\(F_{1}\). The best improvement occurs on the macro-\(F_{1}\) when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, the performance improves in data sets with higher cardinality, density, and diversity of labels. This proves the usefulness of the function on smaller data sets. The results show improvements of more than 10% in terms of macro-\(F_{1}\) in classifiers based on our method in almost all of the cases analyzed. text representation; machine learning; multilabel; text classification +
Footnote †: journal: Medical  
been extended to problems with more than two classes and multilabel, which are closer to real applications. Although there has been an exponential increase in the number of publications based on deep learning models in recent years, it is not possible to completely rule out the techniques and models based on shallow learning. Today there is a debate between the \"white box\" vs. \"black box\" approaches, with advantages and disadvantages for both. Superficial learning highlights the feasibility of working with relatively small data sets and interpretability. Deep learning models stand out for their robustness and good performance. The aim of this research is to evaluate a new term weighting function called relevance frequency for a label (\(rfl\)), initially introduced in [1] for _tf-rfl_, and extended and deepened in [2] as _bin-rfl_. This document presents greater maturity than previous papers, which were previously presented at conferences and in journals, and has a better foundation as well as, a better description of the proposed modification and an improved analysis of the results. Likewise, the analysis of relevant characteristics in the data sets is deepened for a better understanding of the method and choice of the representation. The impact of rendering is shown by considering different performance measures for multilabel classification problems. For this, two types of linear classifiers used in these type of problems were used; these are: Linear Support Vector Machine (SVM) and one-layer artificial neural networks (ANN).