# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Evaluation Criteria  
For a comprehensive understanding of the critical findings of the literature, this section has defined evaluation criteria on which all key papers from the literature have been evaluated and are shown as a comparative study in Tables 1-3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & **No. of Classes** & **Dataset** & **Algorithm/Methodology** &
\begin{tabular}{c} **Evaluation** \\ **Parameters** \\ \end{tabular} & **Results** \\ \hline
[45] & \begin{tabular}{c} Econ (4), Polite (5), \\ RCV1 (14), NVT (2) \\ \end{tabular} & \
\begin{tabular}{c} Econ (62,924), Polite \\ (27,576), RCV1 \\ (100,000), NVT \\ (100,000) \\ \end{tabular} & KNN & F-Measure &
\begin{tabular}{c} Econ (0.41), Polite \\ (0.27), RCV1 (0.76), \\ NVT (0.40) \\ \end{tabular} \\ \hline
[21] & \begin{tabular}{c} 20-Newsgroups \\ (20), Reuters (135) \\ \end{tabular} & \begin{tabular}{c} 20-Newsgroups \\ (20,000), Reuters \\ (21,578) \\ \end{tabular} & Naïve Bayes & \
\begin{tabular}{c} Accuracy, \\ F-Measure \\ \end{tabular} &
\begin{tabular}{c} Accuracy (0.95), \\ F-Measure(0.90) \\ \end{tabular} \\ \hline
[25] & \begin{tabular}{c} 20-Newsgroups \\ (20), Reuters (135) \\ \end{tabular} & \
\begin{tabular}{c} 20-Newsgroups \\ (20,000), Reuters \\ (21,578) \\ \end{tabular} & Bayesian &
\begin{tabular}{c} F-Measure, \\ G-Mean \\ \end{tabular} & Not Reported \\ \hline
[22] & C, Reuters (135) & \begin{tabular}{c} 20-Newsgroups \\ (20,000), Reuters \\ (21,578) \\ \end{tabular} & Survey & \
\begin{tabular}{c} Accuracy, \\ F-Measure \\ \end{tabular} &
\begin{tabular}{c} Accuracy (0.95), \\ F-Measure (0.90) \\ \end{tabular} \\ \hline
[26] & Not Reported & \
\begin{tabular}{c} CiteSeerX (665,483), \\ arXiv (84,172) \\ \end{tabular} & Naïve Bayes, Logistic & F-Measure &
\begin{tabular}{c} CiteSeerX (0.76), \\ arXiv (0.95) \\ \end{tabular} \\ \hline
[23] & \begin{tabular}{c} 20-Newsgroups \\ (20), Reuters-10(10) \\ \end{tabular} & \
\begin{tabular}{c} 20-Newsgroups \\ (16,391), Reuters-10 \\ (7224) \\ \end{tabular} & SVM & F-Measure &
\begin{tabular}{c} 20-Newsgroups \\ (0.76), Reuters-10 \\ (0.91) \\ \end{tabular} \\ \hline
[29] & 4 & 100 Documents & Fuzz-Onto & Accuracy & Accuracy (0.44) \\ \hline
[30] & wiki-20 (5) & Wiki-20 (20) & \
\begin{tabular}{c} Concept Matching-based \\ Approach (CMA) \\ \end{tabular} & Precision, Recall &
\begin{tabular}{c} Precision (0.61), \\ Recall (0.58), \\ F-Measure (0.60) \\ \end{tabular} \\ \hline
[12] & \begin{tabular}{c} 20-Newsgroups (8), \\ SRAA (10), \\ WebKB(10) \\ \end{tabular} & \
\begin{tabular}{c} 20-Newsgroups, \\ SRAA (73,218), WebKB \\ (4199) \\ \end{tabular} & Latent Dirichlet Allocation & F-Measure &
\begin{tabular}{c} 20-Newsgroups \\ (0.92), SRAA (0.85), \\ WebKB (0.71) \\ \end{tabular} \\ \hline
[33] & Not Reported & 100 Features & KNN & Precision, Recall &
\begin{tabular}{c} Precision (0.73), \\ Recall (0.55) \\ \end{tabular} \\ \hline
[44] & \begin{tabular}{c} Reuters (10), \\ WebKB (7) \\ \end{tabular} & \begin{tabular}{c} Reuter (21,578), \\ WebKB (8282) \\ \end{tabular} & \
\begin{tabular}{c} NPE and Particle Swarm \\ Optimization (PSO) \\ \end{tabular} & F-Measure &
\begin{tabular}{c} Reuter (0.94), \\ WebKB (0.89) \\ \end{tabular} \\ \hline
[40] & Not Reported & 2000 Documents & PSO & Accuracy & Accuracy (0.9) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Critical Analysis of Content-based Approaches for Multi-labels.