# Multi-Label Classification Based on Associations
## 3 ML-CBA Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Correlations Type** & **Approach** & **Algorithm** & **Yeast** & **Scene** & **Emotions** & **Flags** \\ \hline \multirow{8}{*}{**Global Correlations**} & \multirow{2}{*}{**1 st Order**} & **ML-CBA** & **0.078** & **0.006** & **0.09** & **0.118** \\ \cline{3-6}  & & **BR** & 0.193 & 0.009 & 0.188 & 0.274 \\  & & **ML-KNN** & 0.193 & 0.085 & 0.262 & 0.284 \\ \hline \multirow{8}{*}{**2 nd Order**} & **BP-MLL** & 0.322 & 0.057 & 0.433 & NG \\  & & **CLR** & 0.226 & 0.101 & 0.214 & NG \\ \cline{1-1} \cline{2-6}  & & **LP** & 0.206 & 0.09 & 0.198 & NG \\  & & **RAKEL** & 0.207 & 0.095 & 0.186 & NG \\  & & **CC** & 0.211 & 0.1 & 0.197 & NG \\  & & **PS** & 0.205 & 0.084 & 0.192 & NG \\  & & **ECC** & 0.619 & 0.47 & 0.63 & NG \\  & & **EPS** & 0.207 & 0.085 & 0.193 & NG \\  & & **BR+** & 0.222 & 0.258 & 0.226 & NG \\ \hline \multirow{2}{*}{**Local Correlations**} & \multirow{2}{*}{**ML-LOC**} & **0.193** & NG & 0.21 & 0.262 \\  & & **LPLC** & 0.202 & NG & 0.197 & 0.279 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation of the proposed ML-CBA algorithm on the regular-sized datasets using Hamming loss metric, with respect to different MLC algorithms. Table 5 depicts the one-error results of the proposed ML-CBA algorithm, with respect to several other MLC algorithms. Table 5 shows clearly that the proposed ML-CBA algorithm has acceptable one-error values compared with several MLC algorithms. Nevertheless, the accuracy of the ML-CBA algorithm is higher than the Accuracy of all other MLC algorithms as depicted in Table 2. This indicates the high benefits of capturing the local positive correlations against capturing global correlations. Furthermore, this is a strong evidence that local correlations are more accurate than global correlations, and thus, have a high influence on the predictive performance of the classification task. #### 3.2.2 Evaluation of the Proposed ML-CBA Algorithm on the Large-sized Datasets

In this subsection, an evaluation of the proposed ML-CBA algorithm on the large-sized multi-label datasets is presented. Two large-sizes datasets (Genbase and TMC2007) are considered in this paper. Four evaluation metrics have been considered in this evaluation (accuracy, Hamming loss, exact match, and one-error). Tables 6-9 shows the evaluation results of the proposed ML-CBA algorithm on the two large-sizes datasets using the previously mentioned evaluation metrics. Table 6 depicts the accuracy evaluation results of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Correlations Type** & **Approach** & **Algorithm** & **Yeast** & **Scene** & **Emotions** & **Flags** \\ \hline \multirow{8}{*}{**Global Correlations**} & \multirow{2}{*}{**1 st Order**} & **ML-CBA** & **0.276** & **0.97** & **0.638** & **0.513** \\ \cline{3-6}  & & **BR** & 0.146 & 0.617 & 0.307 & 0.076 \\  & & **ML-KNN** & 0.189 & 0.643 & 0.143 & 0.098 \\ \hline \multirow{8}{*}{**Global Correlations**} & \multirow{2}{*}{**2 nd Order**} & **BP-MLL** & 0.185 & 0.212 & 0.276 & NG \\  & & **CLR** & NG & NG & NG & NG \\ \cline{3-6}  & & **LP** & 0.194 & 0.696 & 0.351 & 0.123 \\ \cline{3-6}  & & **RAKEL** & 0.163 & 0.662 & 0.341 & NG \\  & & **CC** & 0.196 & 0.669 & 0.349 & NG \\  & & **PS** & 0.258 & 0.717 & 0.367 & NG \\  & & **ECC** & 0.243 & 0.007 & 0.022 & 0.191 \\  & & **EPS** & 0.253 & 0.715 & 0.366 & NG \\ \hline \multirow{2}{*}{**Local Correlations**} & \multirow{2}{*}{**ML-LOC**} & **ML-LOC** & 0.199 & NG & 0.261 & 0.115 \\  & & **LPLC** & 0.186 & NG & 0.303 & 0.123 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The exact match results of the proposed ML-CBA algorithm on the regular-sized datasets with respect to several other MLC algorithms.