## 4 Experiment and Results
### Progressive Learning for Food Category and Ingredient Classification
#### 4.6.1 Multi-Class Classification for Food Category
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & **Accuracy** & **F1-Score (Macc)** & **Precision (Macc)** & **Recall (Macc)** & **Accuracy** & **F1-Score (Macc)** & **Precision (Macc)** & **Recall (Macc)** \\ \hline
**PFDI-Baseline** & Top1 & & & & Top3 & & & & \\ \hline ELM (batch) & 69.77 \(\pm\  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**F1-Score (Macro)**} & \multicolumn{2}{c}{**Precision (Macro)**} & \multicolumn{2}{c}{**Recall (Macro)**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**F1-Score (Macro)**} & \multicolumn{2}{c}{**Precision (Macro)**} & \multicolumn{2}{c}{**Recall (Macro)**} \\ \hline
**VireoFood-172** & Top1 & & & & Top3 & & & & & \\ \hline ELM (batch) & 91.69 \(\pm\) 0.003 & 91.66 \(\pm\) 0.001 & 91.83 \(\pm\) 0.003 & 91.64 \(\pm\) 0.006 & 96.49 \(\pm\) 0.008 & 96.52 \(\pm\) 0.010 & 96.61 \(\pm\) 0.015 & 96.48 \(\pm\) 0.006 \\ RKELM (batch) & 92.01 \(\pm\) 0.006 & 92.01 \(\pm\) 0.011 & 92.30 \(\pm\) 0.012 & 91.90 \(\pm\) 0.010 & 96.92 \(\pm\) 0.005 & 96.88 \(\pm\) 0.005 & 97.08 \(\pm\) 0.006 & 96.74 \(\pm\) 0.004 \\ CIELM & 87.13 \(\pm\) 0.335 & 86.37 \(\pm\) 0.407 & 88.76 \(\pm\) 0.312 & 85.70 \(\pm\) 0.396 & 92.31 \(\pm\) 0.196 & 92.16 \(\pm\) 0.186 & 93.48 \(\pm\) 0.124 & 91.50 \(\pm\) 0.232 \\ ACIELM & 29.12 \(\pm\) 3.12 & 60.69 \(\pm\) 1.58 * & 78.32 \(\pm\) 1.32 * & 29.88 \(\pm\) 1.33 & 31.22 \(\pm\) 2.14 & 64.17 \(\pm\) 1.88 * & 80.46 \(\pm\) 2.41 * & 30.88 \(\pm\) 5.51 \\ ARCIKEM & 92.09 \(\pm\) 0.023 & 92.07 \(\pm\) 0.39 & 92.35 \(\pm\) 0.038 & 91.97 \(\pm\) 0.036 & 96.92 \(\pm\) 0.025 & 96.88 \(\pm\) 0.024 & 97.09 \(\pm\) 0.032 & 96.73 \(\pm\) 0.016 \\ PKELM (RBF) & 92.09 \(\pm\) 0.9 \(\pm\) 0 & 92.09 \(\pm\) 0 & 92.32 \(\pm\) 0 & 92.04 \(\pm\) 0 & 96.85 \(\pm\) 0 & 96.60 \(\pm\) 0 & 96.78 \(\pm\) 0 & 96.49 \(\pm\) 0 \\ PKELM (dis-squared) & 92.17 \(\pm\) 0 & 92.19 \(\pm\) 0 & 92.34 \(\pm\) 0 & 92.16 \(\pm\) 0 & 97.24 \(\pm\) 0 & 97.25 \(\pm\) 0 & 97.39 \(\pm\) 0 & 97.15 \(\pm\) 0 \\ \hline
**UECPOOD100** & Top1 & & & & & & & & \\ \hline ELM (batch) & 90.46 \(\pm\) 0.020 & 84.29 \(\pm\) 0.051 & 86.35 \(\pm\) 0.059 & 83.70 \(\pm\) 0.061 & 97.05 \(\pm\) 0.015 & 94.03 \(\pm\) 0.023 & 95.53 \(\pm\) 0.023 & 93.22 \(\pm\) 0.020 \\ RKELM (batch) & 89.32 \(\pm\) 0.025 & 83.32 \(\pm\) 0.024 & 85.70 \(\pm\) 0.090 & 82.64 \(\pm\) 0.027 & 95.57 \(\pm\) 0.025 & 92.31 \(\pm\) 0.024 & 94.04 \(\pm\) 0.009 & 91.50 \(\pm\) 0.027 \\ CIELM & 87.37 \(\pm\) 0.288 & 79.92 \(\pm\) 0.188 & 83.42 \(\pm\) 0.430 & 79.05 \(\pm\) 0.321 & 95.04 \(\pm\) 0.088 & 92.04 \(\pm\) 0.041 & 92.54 \(\pm\) 0.575 & 98.20 \(\pm\) 0.292 \\ ACIELM & 38.28 \(\pm\) 3.77 & 88.08 \(\pm\) 2.18 & 91.51 \(\pm\) 3.06 & 34.13 \(\pm\) 2.26 & 42.36 \(\pm\) 7.14 & 86.11 \(\pm\) 3.09 & 90.44 \(\pm\) 4.16 & 35.16 \(\pm\) 3.31 \\ PKELM (RBF) & 89.20 \(\pm\) 0.297 & 83.58 \(\pm\) 0.679 & 85.91 \(\pm\) 0.759 & 82.86 \(\pm\) 0.587 & 95.68 \(\pm\) 0.130 & 91.95 \(\pm\) 0.283 & 93.79 \(\pm\) 0.235 & 91.07 \(\pm\) 0.360 \\ PKELM (RBF) & 90.98 \(\pm\) 0 & 84.15 \(\pm\) 0 & 86.04 \(\pm\) 0 & 83.57 \(\pm\) 0 & 96.76 \(\pm\) 0 & 93.66 \(\pm\) 0 & 94.99 \(\pm\) 0 & 92.98 \(\pm\) 0 \\ PKELM (dis-squared) & 91.18 \(\pm\) 0 & 84.46 \(\pm\) 0 & 86.58 \(\pm\) 0 & 83.78 \(\pm\) 0 & 96.56 \(\pm\) 0 & 93.86 \(\pm\) 0 & 95.21 \(\pm\) 0 & 93.30 \(\pm\) 0 \\ \hline
**UECPOOD256** & Top1 & & & & Top3 & & & & \\ \hline ELM (batch) & 78.38 \(\pm\) 0.087 & 77.60 \(\pm\) 0.093 & 80.16 \(\pm\) 0.010 & 78.64 \(\pm\) 0.083 & 89.51 \(\pm\) 0.016 & 89.18 \(\pm\) 0.093 & 90.69 \(\pm\) 0.010 & 89.55 \(\pm\) 0.083 \\ RKELM (batch) & 79.00 \(\pm\) 0.035 & 78.24 \(\pm\)The comparison of the Top 3 accuracy results shows that PKELM with RBF kernel improves performance in contrast to ARCIELM for UECFOOD256, UECFOOD100, PFID-baseline, and Pakistani food datasets by 0.13%, 1.08%, 1.57%, and 2.71%, respectively.