# Multi-Label Classification Based on Associations
## 3 ML-CBA Algorithm
Finally, the high order approach which considers high order correlations among labels has been represented by eight algorithms (LP [56], RAKEL [57], CC [58], PS [59], ECC [58], EPS, ML-LOC [51], and BR+). Further, the chosen algorithms belong to both PTMs (BR, CLR, LP, RAKEL, CC, PS, ECC, EPS, and BR+), and AAMs (ML-KNN and BP-MLL). Furthermore, the chosen algorithms capture both types of correlations: local correlations (ML-LOC and LPLC [3]), and global correlations (LP, RAkEL, CC, PS, EPS, ECC, and BR+). Finally, it is worth mentioning that the Bayesian discretizer [60] has been used as a discretization technique in the ML-CBA algorithm. #### 3.2.1 An Analysis of the Proposed ML-CBA Algorithm Utilizing Datasets of Typical Size

Table 2 shows how the proposed ML-CBA algorithm stacks up against other MLC techniques in terms of accuracy. Table 2 shows that out of the thirteen algorithms considered, the ML-CBA technique has the greatest accuracy value. Finally, ML-CBA beats the other two approaches for capturing local dependencies between labels (ML-LOC and LPLC). Furthermore, when the cardinality of the dataset is high such as in flags and yeast, the advantages of discovering and exploiting the local positive dependencies among labels become more obvious. \"NG\" denotes \"Not Given\" in the tables below,since the original paper where the considered algorithm in these tables did not examine the evaluation metrics or datasets in this paper. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Instances** & **Attributes** & **Labels** & **LCard** \\ \hline Yeast & 2417 & 103 & 14 & 4.327 \\ Scene & 2712 & 294 & 6 & 1.074 \\ Emotions & 593 & 72 & 6 & 1.868 \\ Flags & 194 & 19 & 7 & 3.392 \\ Genbase & 662 & 1186 & 27 & 1.252 \\ TMC2007 & 28,596 & 500 & 22 & 2.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multi-label datasets characteristics. Table 3 depicts the Hamming loss results of the proposed ML-CBA algorithm, with respect to several other MLC algorithms. The results for the Hamming loss evaluation show that ML-CBA algorithm has a superior performance on the four regular-sizes datasets (yeast, scene, emotions, and flags). Table 4 depicts the exact match results of the proposed ML-CBA algorithm, with respect to several other MLC algorithms. Table 4 shows the superior performance of the proposed ML-CBA algorithm, comparing with variety of different MLC algorithms that follow different learning approaches using the exact match metric. ML-CBA overcomes all other algorithms on the four regular-sizes datasets. \begin{table}
\begin{tabular}{c 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** & **0.584** & **0.977** & **0.744** & **0.694** \\ \cline{3-6}  & & **BR** & 0.52 & 0.643 & 0.551 & 0.576 \\  & & **ML-KNN** & 0.52 & 0.691 & 0.366 & 0.555 \\ \hline \multirow{8}{*}{**Global Correlations**} & \multirow{2}{*}{**2 nd Order**} & **BP-MLL** & 0.185 & 0.212 & 0.276 & NG \\  & & **CLR** & 0.514 & 0.695 & 0.557 & NG \\ \cline{3-6}  & & **LP** & 0.53 & 0.735 & 0.584 & NG \\  & & **RAKEL** & 0.493 & 0.694 & 0.592 & NG \\  & & **CC** & 0.521 & 0.736 & 0.584 & NG \\  & & **PS** & 0.533 & 0.751 & 0.599 & NG \\  & & **ECC** & 0.299 & 0.27 & 0.282 & NG \\  & & **EPS** & 0.537 & 0.751 & 0.599 & NG \\  & & **BR+** & 0.4838 & 0.5744 & 0.5537 & NG \\ \hline \multirow{2}{*}{**Local Correlations**} & \multirow{2}{*}{**ML-LOC**} & **ML-LOC** & 0.51 & NG & 0.497 & 0.568 \\  & & **LPLC** & 0.542 & NG & 0.565 & 0.607 \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the proposed ML-CBA algorithm on the regular-sized datasets using accuracy metric, with respect to different MLC algorithms.