# Multilabel Text Classification with Label-Dependent Representation
## 5 Results and Discussion  
In order to compare the effects of using the \(rfl\) function to modify the representation, we carried out different classification experiments using 10 different data sets widely worked on in the literature (Reuters, Ohsumed, Enron, Slashdot, Langdot, Bibtex, Medical, TMC2007, and Science and Education), using _bin-rfl_ and _bin-idf_ representations, both with the Binary Relevance and Label Powerset transformations, and with two different linear classifiers (SVM and ANN). The objective of using these linear classifiers was to evaluate the modification, independent of the classification model. The impact of modifying the representation can be assessed using shallow learning models, which work well under conditions of limited computational complexity, as they do not require prior domain knowledge or experience to extract features from the original text. In turn, smaller data sets of the multilabel classification problem are used, which are widely known in the literature. These sets had already been preprocessed in the standard way. Tables 6-11 show the different methods and their performances in terms of the different performance measures described previously. Regarding the classifiers, the results of the SVMs are at odds with those of the ANNs. Binary Relevance in general performs better than _Label Powerset_, unless the evaluation is in terms of _Accuracy_, where some LP data sets perform better than BR. Regarding rendering, in almost all cases, the _bin-rfl_ rendering has improvements to _bin-idf_. As shown in Tables 6 and 7, an average improvement of over 15% (with SVM) and 40% (with ANN) is obtained in terms of Accuracy. Similarly, improvements of 12% in terms of Hamming Loss are obtained with ANN, as shown in Tables 8 and 9. In [24], the Classification Neural Networks (CNN) model was used, and it was tested in the following data sets: Enron, and Medical and Science, among others. From this, we can compare the results in terms of Hamming Loss. A value of 0.046 is reported for Enron, 0.013 for Medical, and 0.031 for Science. In this comparison, our proposal receives a Hamming Loss value of 0.039, 0.013, and 0.027, respectively. In all three data sets, the proposal evaluated in this research is better than CNN. Finally, as can be seen in Tables 10 and 11, the performance improvement in terms of macro-\(F_{1}\) are 40% (with SVM) and 50% (with ANN), averaged using the _bin-representation rfl_ instead of _bin-idf_ and the Binary Relevance transformation. Here, it can also be mentioned that from [29], performance measures are reported using the same data sets used in this research: Science, Education, and Enron and Bixtex, comparing their PNML proposal, achieving a macro-\(F_{1}\) of 0.298, 0.31, 0.262, and 0.418, respectively. In this comparison, our proposal achieves a macro-\(F_{1}\) of 0.461, 0.285, 0.319, and 0.423, respectively. From the above, it is possible to appreciate that superficial learning models can deliver better results than some deep learning models, in 3 of the 10 data sets compared. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PT** & **Repr.** & **Reuters** & **Ohsumed** & **Enron** & **Slashdot** & **Langlog** \\ \hline BR & _bin-idf_ & 0.247 & 0.402 & 0.080 & 0.257 & 0.240 \\ BR & _bin-rfl_ & 0.200 & **0.414** & 0.105 & 0.325 & 0.334 \\ LP & _bin-idf_ & **0.353** & 0.381 & 0.163 & 0.424 & 0.313 \\ LP & _bin-rfl_ & 0.350 & 0.401 & **0.212** & **0.442** & **0.519** \\ \hline
**PT** & **Repr.** & **Bibtex** & **Medical** & **TMC2007** & **Science** & **Education** \\ \hline BR & _bin-idf_ & 0.122 & 0.577 & 0.220 & 0.290 & 0.242 \\ BR & _bin-rfl_ & 0.138 & 0.413 & 0.216 & 0.293 & 0.171 \\ LP & _bin-idf_ & 0.230 & 0.628 & 0.126 & 0.342 & 0.281 \\ LP & _bin-rfl_ & **0.300** & **0.681** & **0.243** & **0.526** & **0.451** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of different transformations of the problem (PT: BR and LP), and Representations with SVM in terms of _Accuracy_.