# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 6 Results
### Results of Five-Fold Cross-Validation
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l} \hline \hline  & **Fold \#1** & **Fold \#2** & **Fold \#3** & **Fold \#4** & **Fold \#5** & & & & & & & \\ \hline \(k\)-NN\({}_{1}\) & (1, \(m\), in, 0.7140, 0) & (1, c\({}_{3}\), sq, -, 1) & (1, \(s\), sq, -, 0) & (1, c\({}_{3}\), sq, -, 1) & (1, c\({}_{3}\), sq, -, 1) & (1, c\({}_{1}\), sq, -, 0) \\ \(k\)-NN\({}_{2}\) & (1, \(m\), eq, 0.7075, 1) & (1, c\({}_{2}\), sq, -, 1) & (1, c\({}_{3}\), sq, -, 1) & (1, c\({}_{3}\), eq, -, 1) & (1, c\({}_{3}\), eq, -, 1) & (1, c\({}_{3}\), eq, -, 1) & (1, c\({}_{3}\), eq, -, 1) \\ \(k\)-NN\({}_{3}\) & (2, c\({}_{3}\), in, -, 1) & (1, c\({}_{2}\), sq, -, 1) & (1, m, sq, 0.6965, 1) & (1, \(e\), sq, -, 1) & (1, c\({}_{1}\), in, -, 1) \\ \(k\)-NN\({}_{4}\) & (1, c\({}_{3}\), in, -, 1) & (1, m, eq, 0.5485, 1) & (1, c\({}_{3}\), in, -, 1) & (1, c\({}_{3}\), sq, -, 1) & (1, m, sq, 0.6046, 0) \\ \(k\)-NN\({}_{5}\) & (1, c\({}_{3}\), sq, -, 1) & (1, c\({}_{3}\), sq, -, 1) & (2, c\({}_{3}\), in, -, 1) & (1, m, sq, 1.2213, 1) & (3, c\({}_{3}\), sq, -, 1) \\ \(k\)-NN\({}_{6}\) & (1, \(m\), sq, 0.7156, 1) & (1, c\({}_{3}\), in, -, 1) & (1, m, eq, 0.7499, 1) & (1, \(m\), sq, 1.1226, 1) & (1, c\({}_{3}\), eq, -, 1) \\ \(k\)-NN\({}_{7}\) & (1, \(m\), sq, 0.7317, 0) & (1, \(s\), in, -, 0) & (1, \(e\), sq, -, 1) & (1, s, sq, -, 0) & (1, m, eq, 0.8746, 1) \\ \(k\)-NN\({}_{8}\) & (1, \(e\), sq, -, 1) & (1, c\({}_{3}\), sq, -, 1) & (1, m, sq, 0.7044, 1) & (1, c\({}_{1}\), sq, -, 1) & (1, m, in, 0.5390, 1) \\ \(k\)-NN\({}_{9}\) & (1, \(e\), sq, -, 1) & (1, c\({}_{1}\), sq, -, 1) & (1, e, sq, -, 1) & (1, m, eq, 1.5315, 1) & (1, c\({}_{2}\), in, -, 1) \\ \(k\)-NN\({}_{10}\) & (1, \(m\), in, 0.9299, 0) & (1, c\({}_{3}\), in, -, 1) & (1, c\({}_{3}\), sq, -, 1) & (1, \(m\), eq, 1.6912, 0) & (1, m, eq, 1.3160, 0) \\ \(k\)-NN\({}_{11}\) & (1, \(m\), in, eq, 1.7098, 1) & (1, m, eq, 1.7090, 1) & (1, c\({}_{2}\), sq, -, 1) & (1, m, eq, 0.8312, 0) & (1, s, sq, -, 0) \\ \(k\)-NN\({}_{12}\) & (1, \(e\), sq, -, 1) & (1, m, sq, 1.0923, 1) & (1, \(e\), sq, -, 1) & (1, m, sq, 1.1597, 1) & (1, c\({}_{3}\), sq, -, 1) \\ \(k\)-NN\({}_{13}\) & (1, c\({}_{3}\), sq, -, 1) & (1, m, in, 1.4446, 0) & (2, c\({}_{3}\), in, -, 1) & (1, e, sq, -, 1) & (2, c\({}_{1}\), in, -, 0) \\ \(k\)-NN\({}_{14}\) & (1, c\({}_{1}\), eq, -, 0) & (1, \(e\), sq, -, 1) & (1, m, sq, 1.0183, 1) & (1, c\({}_{1}\), sq, -, 1) & (1, m, sq, 1.3201, 1) \\ \(k\)-NN\({}_{15}\) & (1, \(e\), sq, -, 1) & (1, s, eq, -, 0) & (1, c\({}_{3}\), sq, -, 1) & (1, s, sq, -, 0) & (1, c\({}_{3}\), sq, -, 1) \\ \hline \multicolumn{13}{l}{Note: Elements in brackets indicate \(k\), distance metric, distance weighting function, Minkowski distance exponent, and flag to standardize predictors, respectively. In this table, the distance metrics \"cityblock\", \"correlation\", \"cosine\", \"euclidean\", \"minkowski\" and \"seuclidean\" are represented as “\(c_{1}\)\", \"\(c_{2}\)\", \"\(c_{3}\)\", \"\(e\)\", \"\(m\)\", and \"\(s\)\", respectively. The distance weighting functions \"equal\", \"inverse\" and \"squaredinverse\" are denoted as “eq\", “in” and “sq\", respectively. \(k\)-NN: \(k\)-nearest neighbor.} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for the proposed ensemble model in five-fold cross-validation.