# MFCC Selection by LASSO for Honey Bee Classification
## 3 Results
### Classification Quality
The highest accuracy, recall and precision rates were obtained for the models selected by LASSO, resulting with the choice of 69 and 80 features. In those two cases, the accuracy reached 95.3%, recall 85.5%, precision 98.4%, and F1-score 92.1%. The most important indicator from the application point of view was recall, which for all LASSO selection cases had a value around 84-85%. The recall value means the drone bee detectability rate, and this is a reason why we want that value to be the highest possible. The best three results in the sense of drone detection, and the highest recall, were obtained for the 69, 80 and 29 selected-by-LASSO coefficients with recall values: 85.52%, 85.47% and 85.17%, respectively. On the other hand, for the original coefficients sets, presented in Table 3, the recall was only 81.4% for the **MFCC** set, and slightly exceeded 84% for the feature sets **MFCC+Delta** and **MFCC+Delta+DeltaDeltaDelta** containing derivatives. This also shows that in the drone detection task, the first derivatives of MFCC add important information to the audio recording representation in the form of the feature vectors. The cases with LASSO selection of 69 and 80 features significantly outperformed the cases without any feature selection. In case of no feature selection, the highest accuracy was achieved for the basic set of MFCC features without their derivatives, but still in that case, the accuracy reached only 90.6%. The remaining evaluation measures were moderately high precision equal to 86.2% and recall--the worst from all cases--equal to only 81.4%. High precision together with low recall means that the classifier in that case was effective in detecting correctly worker bees but not capable of correctly detecting drone bees, which is the opposite of what we wanted to achieve. That proves that the LASSO selection of MFCC coefficients and their derivatives makes sense in this specific task and improves not only the general classification quality measured simply by the classifier accuracy but also the resulting performance of the drone detection, shown by the higher recall value. The best indicator, showing that application of the LASSO method for MFCC selection improves honey bee classification, is a direct comparison of the following two cases: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Features** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
29 & 88.20\% & 85.17\% & 77.50\% & 82.56\% \\
41 & 87.03\% & 85.00\% & 74.90\% & 81.03\% \\
49 & 85.70\% & 84.80\% & 72.14\% & 79.54\% \\
60 & 95.08\% & 84.96\% & 98.31\% & 91.81\% \\
**69** & **95.26\%** & **85.52\%** & **98.38\%** & **92.16\%** \\
**80** & **95.26\%** & **85.47\%** & **98.44\%** & **92.11\%** \\
93 & 91.44\% & 84.70\% & 86.34\% & 87.97\% \\
101 & 76.10\% & 84.35\% & 56.68\% & 78.38\% \\
111 & 83.10\% & 83.99\% & 67.39\% & 79.92\% \\
120 & 81.15\% & 84.42\% & 63.93\% & 81.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy, recall, precision and F1-score of the honey bee classification for different numbers of selected MFCC features by LASSO. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature Set** & **No. of Features** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline MFCC & 40 & 90.56\% & 81.39\% & 86.20\% & 89.74\% \\ MFCC+Delta & 80 & 82.33\% & 84.15\% & 65.98\% & 80.86\% \\ MFCC+Delta+DeltaDelta & 120 & 81.15\% & 84.42\% & 63.93\% & 81.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy, recall, precision and F1-score of the honey bee classification for the different feature sets—MFCC, MFCC+Delta, and MFCC+DeltaDelta—without applying any feature selection. Figure 9: Classification results for 40 **MFCC** features, 80 **MFCC+Delta** features, and 120 **MFCC+Delta+DeltaDelta** features. * Subset of 80 features selected by LASSO;
* Full set of 80 features in **MFCC+Delta**.