# Multilabel Text Classification with Label-Dependent Representation
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PT** & **Repr.** & **Reuters** & **Ohsumed** & **Enron** & **Slashdot** & **Langlog** \\ \hline BR & _bin-idf_ & 0.230 & 0.379 & 0.223 & 0.312 & 0.075 \\ BR & _bin-rfl_ & **0.239** & **0.426** & **0.230** & **0.377** & **0.279** \\ LP & _bin-idf_ & 0.236 & 0.361 & 0.182 & 0.336 & 0.062 \\ LP & _bin-rfl_ & 0.222 & 0.420 & 0.220 & 0.346 & 0.179 \\ \hline
**TP** & **Repr.** & **Bibtex** & **Medical** & **TMC2007** & **Science** & **Education** \\ \hline BR & _bin-idf_ & 0.322 & **0.379** & 0.492 & 0.286 & **0.236** \\ BR & _bin-rfl_ & **0.351** & 0.345 & **0.527** & 0.293 & 0.210 \\ LP & _bin-idf_ & 0.272 & 0.359 & 0.278 & 0.222 & 0.140 \\ LP & _bin-rfl_ & 0.300 & 0.367 & 0.336 & **0.325** & 0.226 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results of different transformations of the problem (PT: BR and LP), and Representations with SVM in terms of macro-\(F_{1}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PT** & **Repr.** & **Reuters** & **Ohsumed** & **Enron** & **Slashdot** & **Langlog** \\ \hline BR & _bin-idf_ & 0.017 & 0.064 & 0.065 & 0.063 & 0.018 \\ BR & _bin-rfl_ & 0.018 & 0.063 & **0.050** & 0.054 & **0.017** \\ LP & _bin-idf_ & 0.017 & 0.060 & 0.056 & **0.052** & 0.020 \\ LP & _bin-rfl_ & 0.019 & **0.059** & 0.057 & 0.055 & 0.017 \\ \hline
**PT** & **Repr.** & **Bibtex** & **Medical** & **TMC2007** & **Science** & **Education** \\ \hline BR & _bin-idf_ & 0.018 & 0.014 & **0.073** & **0.037** & **0.048** \\ BR & _bin-rfl_ & 0.018 & 0.021 & 0.076 & 0.049 & 0.063 \\ LP & _bin-idf_ & **0.016** & 0.014 & 0.156 & 0.057 & 0.068 \\ LP & _bin-rfl_ & 0.019 & **0.014** & 0.124 & 0.040 & 0.051 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of different transformations of the problem (PT: BR and LP), and Representations with SVM in terms of _Hamming Loss_. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PT** & **Repr.** & **Reuters** & **Ohsumed** & **Enron** & **Slashdot** & **Langlog** \\ \hline BR & _bin-idf_ & 0.015 & 0.067 & 0.057 & 0.055 & 0.016 \\ BR & _bin-rfl_ & **0.013** & 0.064 & **0.039** & **0.042** & **0.012** \\ LP & _bin-idf_ & 0.016 & 0.062 & 0.056 & 0.062 & 0.027 \\ LP & _bin-rfl_ & 0.014 & **0.060** & 0.062 & 0.051 & 0.022 \\ \hline
**PT** & **Repr.** & **Bibtex** & **Medical** & **TMC2007** & **Science** & **Education** \\ \hline BR & _bin-idf_ & **0.013** & 0.013 & 0.064 & **0.027** & 0.040 \\ BR & _bin-rfl_ & 0.014 & **0.013** & **0.063** & 0.029 & 0.047 \\ LP & _bin-idf_ & 0.023 & 0.020 & 0.100 & 0.046 & 0.053 \\ LP & _bin-rfl_ & 0.020 & 0.014 & 0.077 & 0.038 & **0.040** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results of different transformations of the problem (PT: BR and LP), and Representations with ANN in terms of _Hamming Loss_. To present the impact of the \(rfl\) function on the experimental results, Figure 4 graphically shows how, in almost all cases, the _bin-rfl_ representation presents significant improvements in relation to _bin-idf_. This percentage is calculated as the ratio between the difference of the metric with the new representation and the old representation. It can be seen from the figure that the improvements, in many cases, are greater than 20%, in terms of macro-\(F_{1}\). In order to analyze the relationship between the performance improvements introduced by the \(rfl\) function in the different evaluated metrics--Hamming Loss, Accuracy, and macro-\(F_{1}\), with the different characteristics of the sets of documents analyzed (number of labels, number of documents, number of terms in the vocabulary, cardinality, density and diversity)--a correlation analysis of the metrics and characteristics was carried out, identifying the relationships that are explained below. A correlation analysis was carried out by analyzing the output of each classifier and the input characteristics as variables.