# Multilabel Text Classification with Label-Dependent Representation
## 6 Conclusions and Future Scope  
### Conclusions  
The growth of interest in deep learning models does not rule out the techniques and models based on shallow learning, especially when the set of training cases is smaller and the set of features is not very extensive. The \"white box\" versus the \"black box\" approaches have some advantages, especially the feasibility of working with relatively small data sets and the interpretability of the results. Issues in some fields of application are fundamental.  
Classification with multiple labels is an important topic in information retrieval and machine learning, which has become more relevant in recent years. Text representation and classification have traditionally been handled using \(tf\)-\(idf\), due to its simplicity and good performance. However, the \(tf\)-\(idf\) representation does not take into account that the examples may have different labels. The latter is very relevant in data sets with high cardinality and label diversity.  
Changes in the input representation to classifiers can use knowledge about the problem, its domain, a particular label, or the category to which the document belongs. The \(rfl\)  
Figure 7: Correlation between the performance improvements in terms of macro-\(F_{1}\) and the different characteristics of the data set (number of labels, number of documents, number of vocabulary terms, cardinality, density, and diversity).  
function can be written to solve a particular problem directly and without complex problem transformations, using the information from the examples and their different labels.  
In this work, we have introduced the \(rfl\) function to build new text representations for the multilabel classification approach. This function allows for discriminating the terms that best describe a category, in contrast to other categories, thus taking advantage of the characteristics of the domain of documents that make up the corpus.  
This proposal was evaluated using two different linear classifiers, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), with the aim of evaluating the impact of the function on simple classifiers. In turn, the impact was evaluated on 10 different sets of texts, which correspond to medical scientific articles, journalistic documents, medical diagnostic reports, email messages, and web pages. A comparison with \(bin-idf\) was made, and two transformations of the multilabeling problem were used (Binary Relevance and Label Powerset).  
The performance of this function shows an improvement in almost all cases, using the Binary Relevance transformation and Support Vector Machines. Only to the extent of Hamming Loss was it better to use Label Powerset and Support Vector Machines.  
The greatest impact of using the \(rfl\) function occurs on the macro-\(F_{1}\) performance metric when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, this measure improves on data sets with higher cardinalities, densities, and diversities of labels. This reflects the utility of the function on smaller data sets.  
We believe that the contribution of the use of the \(rfl\) function, when using it as a weighting factor to modify the multilabel representation, is due to a better resolution of the considered problem, since it is capable of making a better identification of the terms in the documents, which is reflected in a better performance of the classification models. From the perspective of machine learning applications and the increasing rate of their adoption in the industry, one must consider the need to develop computationally lightweight models that can be implemented under affordable technological conditions for companies of different sizes.