## 4 Experiments
### Results under Original Data  
In this section, the experimental results of our proposed method and the above-mentioned multi-label classification algorithms are analyzed and discussed in detail. We evaluated the performance of our proposed method and the other comparative multi-label classification algorithms on different data sets using Example-F1, Micro-F1 and Hamming loss evaluation metrics. The specific results of this experimental evaluation are summarized in Tables 2-4  
Table 2 presents the evaluation performance of each comparative algorithm on the dataset using Example-F1 evaluation metric. From the results in the table, it can be observed that, compared to the other multi-label classification methods, our proposed method demonstrated superior performance with respect to the Example-F1 evaluation index on the six multi-label datasets. In particular, the evaluation performance of the proposed algorithm on the Image, Scene, Yeast, Cal500, Rcv1s1 and Core116k4 datasets is improved by 11.4%, 9.41%, 10.34%, 1.3% and 0.73%, respectively, compared with the other comparative algorithms. Similarly, Table 3 captures the performance of our proposed method and that of the comparative algorithms using the Micro-F1 evaluation metric. In images, scenes, and yeast datasets, the algorithm proposed in this paper obtained a better performance, whilst RBRL and MLR algorithms also demonstrated relatively good performance. This can be attributed to the fact that, RBRL algorithm combines sorting support vector machines and binary correlation with robust low-order learning, whereas the MLR algorithm is systematically studied from two complementary perspectives: the consistency of learning algorithm and the generalized error bound. These two make the classifier more robust when dealing with complex datasets. Our proposed algorithm recorded performance values which are 1.15%, 2.03% and 0.24% higher than that of MLR algorithm, and 0.95%, 1.47% and 0.27% higher than the RBRL algorithm, respectively. The proposed algorithm's ability to combine feature selection with a multi label SVM classifier culminates in the generation of a more robust SVM classifiers under complex samples. In a nutshell, with respect to the Micro-F1 evaluation metric, our method demonstrated a significant superior experimental results in high-dimensional feature space. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hamming loss** & **Rank-SVM** & **ML-KNN** & **CPNL** & **MLR** & **RBRL** & **Proposed** \\ \hline Image & 0.2869 & 0.3174 & 0.3402 & 0.1768 & 0.1712 & **0.1695** \\ Scene & 0.1427 & 0.1203 & 0.1557 & 0.1143 & 0.1117 & **0.1103** \\ Yeast & 0.2314 & 0.3104 & 0.2253 & 0.2027 & 0.2019 & **0.2006** \\ Cal500 & 0.1582 & 0.1732 & 0.1794 & 0.1763 & 0.1642 & **0.1578** \\ Rcv1s1 & 0.0413 & 0.0326 & 0.0415 & 0.0397 & 0.0326 & **0.0304** \\ Core116k4 & 0.0214 & 0.0212 & 0.0463 & 0.0218 & 0.0245 & **0.0201** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results of competitive algorithms on the datasets using the Hamming loss metric (\(\downarrow\)). Figure 2: Overall average ranks of the competitive methods on all evaluation metrics. The performance analysis of our proposed method and the other comparative algorithms on the selected experimental dataset with respect to the Hamming loss metric is recorded in Table 4. Hamming loss is used to examine the misclassification of samples on a single marker, where relevant markers do not appear in the predicted marker set or irrelevant markers appear in the predicted marker set. Generally, our proposed method significantly outperformed the other comparative algorithms in almost all the dataset used for the experiment. However, the improvement was not significant on the Core116k4 dataset, which may be largely attributed to the large sample size of the dataset. As shown in Table 5 and Figure 2, the algorithm proposed in this paper achieved better performance than other methods in terms of overall indicators.